@article{alman,
 author = {Alman, Josh},
 title = {Limits on the Universal Method for Matrix Multiplication},
 year = {2021},
 pages = {1--30},
 doi = {10.4086/toc.2021.v017a001},
 publisher = {Theory of Computing},
 journal = {Theory of Computing},
 volume = {17},
 number = {1},
 URL = {https://theoryofcomputing.org/articles/v017a001},
}

@inproceedings{renyi1961measures,
  author = {Rényi, Alfréd},
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@inproceedings{deng2009imagenet,
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  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
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@article{han2022rarity,
  title={Rarity score: A new metric to evaluate the uncommonness of synthesized images},
  author={Han, Jiyeon and Choi, Hwanil and Choi, Yunjey and Kim, Junho and Ha, Jung-Woo and Choi, Jaesik},
  journal={arXiv preprint arXiv:2206.08549},
  year={2022}
}

@article{dong2023optimal,
  title={Optimal randomized approximations for matrix-based r{\'e}nyi’s entropy},
  author={Dong, Yuxin and Gong, Tieliang and Yu, Shujian and Li, Chen},
  journal={IEEE Transactions on Information Theory},
  year={2023},
  publisher={IEEE}
}

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  author={Shawe-Taylor, J. and Williams, C.K.I. and Cristianini, N. and Kandola, J.},
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  volume={51},
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@article{jiralerspong2024feature,
  title={Feature likelihood score: Evaluating the generalization of generative models using samples},
  author={Jiralerspong, Marco and Bose, Joey and Gemp, Ian and Qin, Chongli and Bachrach, Yoram and Gidel, Gauthier},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}


@article{ospanov2024dissecting,
  title={Dissecting CLIP: Decomposition with a Schur Complement-based Approach},
  author={Ospanov, Azim and Jalali, Mohammad and Farnia, Farzan},
  journal={arXiv preprint arXiv:2412.18645},
  year={2024}
}

@inproceedings{hu2025multi,
  title={A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models},
  author={Hu, Xiaoyan and Leung, Ho-fung and Farnia, Farzan},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1864--1872},
  year={2025},
  organization={PMLR}
}


@inproceedings{zhang2025unveiling,
  title={Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach},
  author={Zhang, Jingwei and Jalali, Mohammad and Li, Cheuk Ting and Farnia, Farzan},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={8269--8278},
  year={2025}
}

@misc{wu2025fusingcrossmodalunimodalrepresentations,
      title={Fusing Cross-modal and Uni-modal Representations: A Kronecker Product Approach}, 
      author={Youqi Wu and Jingwei Zhang and Farzan Farnia},
      year={2025},
      eprint={2506.08645},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2506.08645}, 
}

@article{wang2023distributed,
  title={On the Distributed Evaluation of Generative Models},
  author={Wang, Zixiao and Farnia, Farzan and Lin, Zhenghao and Shen, Yunheng and Yu, Bei},
  journal={arXiv preprint arXiv:2310.11714},
  year={2023}
}

@article{gong2025kernel,
  title={Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models},
  author={Gong, Shizhan and Jiang, Yankai and Dou, Qi and Farnia, Farzan},
  journal={arXiv preprint arXiv:2506.02557},
  year={2025}
}

@article{jalali2025towards,
  title={Towards an Explainable Comparison and Alignment of Feature Embeddings},
  author={Jalali, Mohammad and Nia, Bahar Dibaei and Farnia, Farzan},
  journal={arXiv preprint arXiv:2506.06231},
  year={2025}
}

@article{zhang2024interpretable,
  title={An interpretable evaluation of entropy-based novelty of generative models},
  author={Zhang, Jingwei and Li, Cheuk Ting and Farnia, Farzan},
  journal={arXiv preprint arXiv:2402.17287},
  year={2024}
}

@article{hu2024online,
  title={An Online Learning Approach to Prompt-based Selection of Generative Models},
  author={Hu, Xiaoyan and Leung, Ho-fung and Farnia, Farzan},
  journal={arXiv preprint arXiv:2410.13287},
  year={2024}
}

@article{rezaei2024more,
  title={Be More Diverse than the Most Diverse: Online Selection of Diverse Mixtures of Generative Models},
  author={Rezaei, Parham and Farnia, Farzan and Li, Cheuk Ting},
  journal={arXiv preprint arXiv:2412.17622},
  year={2024}
}

@article{jalali2024conditional,
  title={Conditional Vendi Score: An Information-Theoretic Approach to Diversity Evaluation of Prompt-based Generative Models},
  author={Jalali, Mohammad and Ospanov, Azim and Gohari, Amin and Farnia, Farzan},
  journal={arXiv preprint arXiv:2411.02817},
  year={2024}
}


@mastersthesis{linnainmaa,
  author  = "S. Linnainmaa",
  title   = "The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors",
  school  = "University of Helsinki",
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}

@article{christandl-etal,
 author = {Christandl, Matthias and Vrana, P\'eter and Zuiddam, Jeroen},
 title = {Barriers for Fast Matrix Multiplication from Irreversibility},
 year = {2021},
 pages = {1--32},
 doi = {10.4086/toc.2021.v017a002},
 publisher = {Theory of Computing},
 journal = {Theory of Computing},
 volume = {17},
 number = {2},
 URL = {https://theoryofcomputing.org/articles/v017a002},
}

@article{AISW,
  author={Acharya, Jayadev and Issa, Ibrahim and Shende, Nirmal V. and Wagner, Aaron B.},
  journal={IEEE Journal on Selected Areas in Information Theory}, 
  title={Estimating Quantum Entropy}, 
  year={2020},
  volume={1},
  number={2},
  pages={454-468},
  doi={10.1109/JSAIT.2020.3015235}}
  
@article{wu-yang,
  author = {Wu, Yihong and Yang, Pengkun},
  title = {Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation},
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  publisher = {IEEE Press},
  volume = {62},
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  journal = {IEEE Trans. Inf. Theor.},
  month = {jun},
  pages = {3702–3720},
  numpages = {19}
  }

@inproceedings{VV11,
  title={Estimating the unseen: an n/log (n)-sample estimator for entropy and support size, shown optimal via new CLTs},
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}

@article{paninski,
  title={Estimation of entropy and mutual information},
  author={Paninski, Liam},
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  publisher={MIT Press}
}

@Article{baur-strassen,
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@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}

@inproceedings{
pillutla2021mauve,
title={{MAUVE}: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Krishna Pillutla and Swabha Swayamdipta and Rowan Zellers and John Thickstun and Sean Welleck and Yejin Choi and Zaid Harchaoui},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=Tqx7nJp7PR}
}

@article{pillutla-etal:mauve:jmlr2023,
  title={{MAUVE Scores for Generative Models: Theory and Practice}},
  author={Pillutla, Krishna and Liu, Lang and Thickstun, John and Welleck, Sean and Swayamdipta, Swabha and Zellers, Rowan and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
  journal={JMLR},
  year={2023}
}

@inproceedings{jalali_information-theoretic_2023,
	title = {An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions},
	volume = {36},
	pages = {9931--9943},
	booktitle = {Advances in Neural Information Processing Systems},

	author = {Jalali, Mohammad and Li, Cheuk Ting and Farnia, Farzan},
    year={2023},
	urldate = {2024-05-15},
	date = {2023-12-15},
	langid = {english},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/254YPTCG/Jalali et al. - 2023 - An Information-Theoretic Evaluation of Generative .pdf:application/pdf},
}

@inproceedings{ospanov_fkea_2024,
	title = {Towards a Scalable Reference-Free Evaluation of Generative Models},
	volume = {38},
	booktitle = {Advances in Neural Information Processing Systems},

	author = {Ospanov, Azim and Zhang, Jingwei and Jalali, Mohammad and Cao, Xuenan and Bogdanov, Andrej and Farnia, Farzan},
    year={2024},
	langid = {english},
}

@inproceedings{friedman_vendi_2023,
	title = {The Vendi Score: A Diversity Evaluation Metric for Machine Learning},
    year={2023},
    booktitle = {Transactions on Machine Learning Research},
	author = {Friedman, Dan and Dieng, Adji Bousso},
}


@misc{bach_information_2022,
	title = {Information {Theory} with {Kernel} {Methods}},
	url = {http://arxiv.org/abs/2202.08545},
	doi = {10.48550/arXiv.2202.08545},
	abstract = {We consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy and relative entropy of these operators are intimately related to the usual notions of Shannon entropy and relative entropy, and share many of their properties. They come together with efficient estimation algorithms from various oracles on the probability distributions. We also consider product spaces and show that for tensor product kernels, we can define notions of mutual information and joint entropies, which can then characterize independence perfectly, but only partially conditional independence. We finally show how these new notions of relative entropy lead to new upper-bounds on log partition functions, that can be used together with convex optimization within variational inference methods, providing a new family of probabilistic inference methods.},
	urldate = {2024-09-30},
	publisher = {arXiv},
	author = {Bach, Francis},
	month = aug,
	year = {2022},
	note = {arXiv:2202.08545 [cs, math, stat]},
	keywords = {Computer Science - Information Theory, Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\SUC79NIA\\Bach - 2022 - Information Theory with Kernel Methods.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\5UJFSB5V\\2202.html:text/html},
}


@inproceedings{zhang_interpretable_2024,
	title = {An Interpretable Evaluation of Entropy-based Novelty of Generative Models},
	url = {http://arxiv.org/abs/2402.17287},
	doi = {10.48550/arXiv.2402.17287},
	abstract = {The massive developments of generative model frameworks and architectures require principled methods for the evaluation of a model's novelty compared to a reference dataset or baseline generative models. While the recent literature has extensively studied the evaluation of the quality, diversity, and generalizability of generative models, the assessment of a model's novelty compared to a baseline model has not been adequately studied in the machine learning community. In this work, we focus on the novelty assessment under multi-modal generative models and attempt to answer the following question: Given the samples of a generative model \${\textbackslash}mathcal\{G\}\$ and a reference dataset \${\textbackslash}mathcal\{S\}\$, how can we discover and count the modes expressed by \${\textbackslash}mathcal\{G\}\$ more frequently than in \${\textbackslash}mathcal\{S\}\$. We introduce a spectral approach to the described task and propose the Kernel-based Entropic Novelty ({KEN}) score to quantify the mode-based novelty of distribution \$P\_{\textbackslash}mathcal\{G\}\$ with respect to distribution \$P\_{\textbackslash}mathcal\{S\}\$. We analytically interpret the behavior of the {KEN} score under mixture distributions with sub-Gaussian components. Next, we develop a method based on Cholesky decomposition to compute the {KEN} score from observed samples. We support the {KEN}-based quantification of novelty by presenting several numerical results on synthetic and real image distributions. Our numerical results indicate the success of the proposed approach in detecting the novel modes and the comparison of state-of-the-art generative models.},
    booktitle = {International Conference on Machine Learning (ICML 2024)},
	author = {Zhang, Jingwei and Li, Cheuk Ting and Farnia, Farzan},
	urldate = {2024-05-15},
	date = {2024-02-27},
	eprinttype = {arxiv},
	eprint = {2402.17287 [cs, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/Users/azimospanov/Zotero/storage/AKWP8254/Zhang et al. - 2024 - An Interpretable Evaluation of Entropy-based Novel.pdf:application/pdf;arXiv.org Snapshot:/Users/azimospanov/Zotero/storage/AU8W86Q2/2402.html:text/html},
}

@misc{shaib_standardizing_2024,
	title = {Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores},
	url = {http://arxiv.org/abs/2403.00553},
	doi = {10.48550/arXiv.2403.00553},
	shorttitle = {Standardizing the Measurement of Text Diversity},
	abstract = {The diversity across outputs generated by large language models shapes the perception of their quality and utility. Prompt leaks, templated answer structure, and canned responses across different interactions are readily noticed by people, but there is no standard score to measure this aspect of model behavior. In this work we empirically investigate diversity scores on English texts. We find that computationally efficient compression algorithms capture information similar to what is measured by slow to compute \$n\$-gram overlap homogeneity scores. Further, a combination of measures -- compression ratios, self-repetition of long \$n\$-grams and Self-{BLEU} and {BERTScore} -- are sufficient to report, as they have low mutual correlation with each other. The applicability of scores extends beyond analysis of generative models; for example, we highlight applications on instruction-tuning datasets and human-produced texts. We release a diversity score package to facilitate research and invite consistency across reports.},
	number = {{arXiv}:2403.00553},
	publisher = {{arXiv}},
	author = {Shaib, Chantal and Barrow, Joe and Sun, Jiuding and Siu, Alexa F. and Wallace, Byron C. and Nenkova, Ani},
	urldate = {2024-05-15},
	date = {2024-03-01},
	eprinttype = {arxiv},
	eprint = {2403.00553 [cs]},
	keywords = {Computer Science - Computation and Language},
	file = {arXiv Fulltext PDF:/Users/azimospanov/Zotero/storage/AZVGTVVZ/Shaib et al. - 2024 - Standardizing the Measurement of Text Diversity A.pdf:application/pdf;arXiv.org Snapshot:/Users/azimospanov/Zotero/storage/M4IBHW6Z/2403.html:text/html},
}

@inproceedings{danilevsky_survey_2020,
	location = {Suzhou, China},
	title = {A Survey of the State of Explainable {AI} for Natural Language Processing},
	url = {https://aclanthology.org/2020.aacl-main.46},
	abstract = {Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable {AI} ({XAI}), considered within the domain of Natural Language Processing ({NLP}). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for {NLP} model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.},
	eventtitle = {{AACL} 2020},
	pages = {447--459},
	booktitle = {Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
	publisher = {Association for Computational Linguistics},
	author = {Danilevsky, Marina and Qian, Kun and Aharonov, Ranit and Katsis, Yannis and Kawas, Ban and Sen, Prithviraj},
	editor = {Wong, Kam-Fai and Knight, Kevin and Wu, Hua},
	urldate = {2024-05-15},
	date = {2020-12},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/RPRU7SCL/Danilevsky et al. - 2020 - A Survey of the State of Explainable AI for Natura.pdf:application/pdf},
}

@inproceedings{brown_language_2020,
	title = {Language Models are Few-Shot Learners},
	volume = {33},
	url = {https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html},
	abstract = {We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches. Specifically, we train {GPT}-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.  For all tasks, {GPT}-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model.  {GPT}-3 achieves strong performance on many {NLP} datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where {GPT}-3's few-shot learning still struggles, as well as some datasets where {GPT}-3 faces methodological issues related to training on large web corpora.},
	pages = {1877--1901},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and {McCandlish}, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
	urldate = {2024-05-15},
	date = {2020},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/4ZDVKJG7/Brown et al. - 2020 - Language Models are Few-Shot Learners.pdf:application/pdf},
}

@inproceedings{szegedy_rethinking_2016,
	title = {Rethinking the Inception Architecture for Computer Vision},
    author = {Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew},
	booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
    year={2016}
}

@inproceedings{karras2021aliasfree,
  title={Alias-free generative adversarial networks},
  author={Karras, Tero and Aittala, Miika and Laine, Samuli and H{\"a}rk{\"o}nen, Erik and Hellsten, Janne and Lehtinen, Jaakko and Aila, Timo},
  booktitle={Advances in Neural Information Processing Systems},
  volume={34},
  pages={852--863},
  year={2021},
  publisher={Curran Associates, Inc.}
}

@inproceedings{karras2019style,
  title={A Style-Based Generator Architecture for Generative Adversarial Networks},
  author={Karras, Tero and Laine, Samuli and Aila, Timo},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={4401--4410}
}

@inproceedings{russakovsky_imagenet_2015,
	title = {{ImageNet} Large Scale Visual Recognition Challenge},
    year={2015},
	url = {http://arxiv.org/abs/1409.0575},
	doi = {10.1007/s11263-015-0816-y},
	abstract = {The {ImageNet} Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.},
	pages = {211--252},
	number = {3},
	booktitle = {International Journal of Computer Vision ({IJCV})},
	author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and Berg, Alexander C. and Fei-Fei, Li},
	urldate = {2024-05-15},
	date = {2015-01-29},
	eprinttype = {arxiv},
	eprint = {1409.0575 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, I.4.8, I.5.2},
	file = {arXiv Fulltext PDF:/Users/azimospanov/Zotero/storage/TH825ZBD/Russakovsky et al. - 2015 - ImageNet Large Scale Visual Recognition Challenge.pdf:application/pdf;arXiv.org Snapshot:/Users/azimospanov/Zotero/storage/4VFVTRQ5/1409.html:text/html},
}

@inproceedings{rahimi_random_2007,
	title = {Random Features for Large-Scale Kernel Machines},
	volume = {20},
	url = {https://papers.nips.cc/paper_files/paper/2007/hash/013a006f03dbc5392effeb8f18fda755-Abstract.html},
    year={2007},
	abstract = {To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user speciﬁed shift- invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classiﬁcation and regression tasks linear machine learning al- gorithms applied to these features outperform state-of-the-art large-scale kernel machines.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Rahimi, Ali and Recht, Benjamin},
	urldate = {2024-05-15},
	date = {2007},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/FW2YGYZJ/Rahimi and Recht - 2007 - Random Features for Large-Scale Kernel Machines.pdf:application/pdf},
}

@inproceedings{papineni_bleu_2002,
	location = {{USA}},
	title = {{BLEU}: a method for automatic evaluation of machine translation},
	url = {https://dl.acm.org/doi/10.3115/1073083.1073135},
	doi = {10.3115/1073083.1073135},
    year={2002},
	series = {{ACL} '02},
	shorttitle = {{BLEU}},
	abstract = {Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.},
	pages = {311--318},
	booktitle = {Proceedings of the 40th Annual Meeting on Association for Computational Linguistics},
	publisher = {Association for Computational Linguistics},
	author = {Papineni, Kishore and Roukos, Salim and Ward, Todd and Zhu, Wei-Jing},
	urldate = {2024-05-15},
	date = {2002-07-06},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/LUKV2AEA/Papineni et al. - 2002 - BLEU a method for automatic evaluation of machine.pdf:application/pdf},
}

@inproceedings{zhu_texygen_2018,
	location = {New York, {NY}, {USA}},
	title = {Texygen: A Benchmarking Platform for Text Generation Models},
	isbn = {978-1-4503-5657-2},
	url = {https://doi.org/10.1145/3209978.3210080},
    year={2018},
	doi = {10.1145/3209978.3210080},
	series = {{SIGIR} '18},
	shorttitle = {Texygen},
	abstract = {We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and improve the reproductivity and reliability of future research work in text generation.},
	pages = {1097--1100},
	booktitle = {The 41st International {ACM} {SIGIR} Conference on Research \& Development in Information Retrieval},
	publisher = {Association for Computing Machinery},
	author = {Zhu, Yaoming and Lu, Sidi and Zheng, Lei and Guo, Jiaxian and Zhang, Weinan and Wang, Jun and Yu, Yong},
	urldate = {2024-05-15},
	date = {2018-06-27},
	keywords = {benchmarking, evaluation metrics, text generation},
}

@inproceedings{kynkaanniemi_improved_2019,
	title = {Improved Precision and Recall Metric for Assessing Generative Models},
	volume = {32},
    year={2019},
	abstract = {The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in {StyleGAN} and {BigGAN} by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of {StyleGAN} to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Kynkäänniemi, Tuomas and Karras, Tero and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo},
	urldate = {2024-05-15},
	date = {2019},
}

@inproceedings{salimans_improved_2016,
	title = {Improved Techniques for Training {GANs}},
	volume = {29},
	url = {https://papers.nips.cc/paper_files/paper/2016/hash/8a3363abe792db2d8761d6403605aeb7-Abstract.html},
    year={2016},
	abstract = {We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks ({GANs}) framework. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on {MNIST}, {CIFAR}-10 and {SVHN}. The generated images are of high quality as confirmed by a visual Turing test: Our model generates {MNIST} samples that humans cannot distinguish from real data, and {CIFAR}-10 samples that yield a human error rate of 21.3\%. We also present {ImageNet} samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of {ImageNet} classes.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi and Chen, Xi},
	urldate = {2024-05-15},
	date = {2016},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/9HH8DP4B/Salimans et al. - 2016 - Improved Techniques for Training GANs.pdf:application/pdf},
}

@article{kang_studiogan_2023,
	title = {{StudioGAN}: A Taxonomy and Benchmark of {GANs} for Image Synthesis},
	volume = {45},
	issn = {0162-8828},
	url = {https://www.computer.org/csdl/journal/tp/2023/12/10224337/1PI5MZRQME0},
	doi = {10.1109/TPAMI.2023.3306436},
	shorttitle = {{StudioGAN}},
	abstract = {Generative Adversarial Network ({GAN}) is one of the state-of-the-art generative models for realistic image synthesis. While training and evaluating {GAN} becomes increasingly important, the current {GAN} research ecosystem does not provide reliable benchmarks for which the evaluation is conducted consistently and fairly. Furthermore, because there are few validated {GAN} implementations, researchers devote considerable time to reproducing baselines. We study the taxonomy of {GAN} approaches and present a new open-source library named {StudioGAN}. {StudioGAN} supports 7 {GAN} architectures, 9 conditioning methods, 4 adversarial losses, 12 regularization modules, 3 differentiable augmentations, 7 evaluation metrics, and 5 evaluation backbones. With our training and evaluation protocol, we present a large-scale benchmark using various datasets ({CIFAR}10, {ImageNet}, {AFHQv}2, {FFHQ}, and Baby/Papa/Granpa-{ImageNet}) and 3 different evaluation backbones ({InceptionV}3, {SwAV}, and Swin Transformer). Unlike other benchmarks used in the {GAN} community, we train representative {GANs}, including {BigGAN} and {StyleGAN} series in a unified training pipeline and quantify generation performance with 7 evaluation metrics. The benchmark evaluates other cutting-edge generative models (e.g., {StyleGAN}-{XL}, {ADM}, {MaskGIT}, and {RQ}-Transformer). {StudioGAN} provides {GAN} implementations, training, and evaluation scripts with the pre-trained weights. {StudioGAN} is available at https://github.com/{POSTECH}-{CVLab}/{PyTorch}-{StudioGAN}.},
	pages = {15725--15742},
	number = {12},
	journaltitle = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
	author = {Kang, Minguk and Shin, Joonghyuk and Park, Jaesik},
	urldate = {2024-05-15},
	date = {2023-12-01},
	note = {Publisher: {IEEE} Computer Society},
	file = {Submitted Version:/Users/azimospanov/Zotero/storage/XL6ABYP8/Kang et al. - 2023 - StudioGAN A Taxonomy and Benchmark of GANs for Im.pdf:application/pdf},
}

@inproceedings{williams2000nystrom,
  title={Using the Nyström method to speed up kernel machines},
  author={Williams, Christopher and Seeger, Matthias},
  booktitle={Advances in neural information processing systems},
  pages={682--688},
  year={2000}
}

@inproceedings{fine2001efficient,
  title={Efficient SVM training using low-rank kernel representations},
  author={Fine, Shai and Scheinberg, Katya},
  booktitle={Journal of Machine Learning Research (JMLR)},
  pages={243--250},
  year={2001}
}

@inproceedings{bach2002kernel,
  title={Kernel independent component analysis},
  author={Bach, Francis R and Jordan, Michael I},
  booktitle={Journal of Machine Learning Research},
  volume={3},
  pages={1--48},
  year={2002}
}

@inproceedings{curmatrix_michael,
author = {Michael W. Mahoney  and Petros Drineas },
title = {CUR matrix decompositions for improved data analysis},
booktitle = {Proceedings of the National Academy of Sciences},
volume = {106},
number = {3},
pages = {697-702},
year = {2009}}



@inproceedings{heusel_gans_2018,
	title = {{GANs} Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium},
	volume = {30},
    year={2018},
	abstract = {Generative Adversarial Networks ({GANs}) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of {GAN} training has still not been proved. We propose a two time-scale update rule ({TTUR}) for training {GANs} with stochastic gradient descent on arbitrary {GAN} loss functions. {TTUR} has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the {TTUR} converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of {GANs} at image generation, we introduce the `Fréchet Inception Distance'' ({FID}) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, {TTUR} improves learning for {DCGANs} and Improved Wasserstein {GANs} ({WGAN}-{GP}) outperforming conventional {GAN} training on {CelebA}, {CIFAR}-10, {SVHN}, {LSUN} Bedrooms, and the One Billion Word Benchmark.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
	urldate = {2024-05-15},
	date = {2017},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/A9WL499D/Heusel et al. - 2017 - GANs Trained by a Two Time-Scale Update Rule Conve.pdf:application/pdf},
}

@inproceedings{naeem_reliable_2020,
	title = {Reliable fidelity and diversity metrics for generative models},
	volume = {119},
    year={2020},
	series = {{ICML}'20},
	abstract = {Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance ({FID}) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those properties separately. In this paper, we show that even the latest version of the precision and recall metrics are not reliable yet; for example, they fail to detect the match between two identical distributions, they are not robust against outliers, and the evaluation hyperparameters are selected arbitrarily. We propose density and coverage metrics that solve the above issues. We analytically and experimentally show that density and coverage provide more interpretable and reliable signals for practitioners than the existing metrics. Code: github.com/clovaai/generative-evaluation-prdc.},
	pages = {7176--7185},
	booktitle = {Proceedings of the 37th International Conference on Machine Learning},
	publisher = {{JMLR}.org},
	author = {Naeem, Muhammad Ferjad and Oh, Seong Joon and Uh, Youngjung and Choi, Yunjey and Yoo, Jaejun},
	urldate = {2024-05-15},
	date = {2020-07-13},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/JN4T954X/Naeem et al. - 2020 - Reliable fidelity and diversity metrics for genera.pdf:application/pdf},
}

@inproceedings{sajjadi_assessing_2018,
	title = {Assessing Generative Models via Precision and Recall},
	volume = {31},
    year={2018},
	abstract = {Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, such as the Frechet Inception Distance ({FID}), correlate well with the perceived quality of samples and are sensitive to mode dropping. However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and {FID}. To demonstrate the practical utility of the proposed approach we perform an empirical study on several variants of Generative Adversarial Networks and Variational Autoencoders. In an extensive set of experiments we show that the proposed metric is able to disentangle the quality of generated samples from the coverage of the target distribution.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Sajjadi, Mehdi S. M. and Bachem, Olivier and Lucic, Mario and Bousquet, Olivier and Gelly, Sylvain},
	urldate = {2024-05-15},
	date = {2018},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/9FJ2ZTZX/Sajjadi et al. - 2018 - Assessing Generative Models via Precision and Reca.pdf:application/pdf},
}

@article{zhang_understanding_2021,
	title = {Understanding deep learning (still) requires rethinking generalization},
	volume = {64},
	issn = {0001-0782},
	url = {https://dl.acm.org/doi/10.1145/3446776},
	doi = {10.1145/3446776},
	abstract = {Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models. We supplement this republication with a new section at the end summarizing recent progresses in the field since the original version of this paper.},
	pages = {107--115},
	number = {3},
	journaltitle = {Communications of the {ACM}},
	shortjournal = {Commun. {ACM}},
	author = {Zhang, Chiyuan and Bengio, Samy and Hardt, Moritz and Recht, Benjamin and Vinyals, Oriol},
	urldate = {2024-05-15},
	date = {2021-02-22},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/FXV5XPWM/Zhang et al. - 2021 - Understanding deep learning (still) requires rethi.pdf:application/pdf},
}

@article{brock_large_2018,
	title = {Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},
	url = {https://www.semanticscholar.org/paper/Large-Scale-GAN-Training-for-High-Fidelity-Natural-Brock-Donahue/22aab110058ebbd198edb1f1e7b4f69fb13c0613},
	abstract = {Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as {ImageNet} remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on {ImageNet} at 128x128 resolution, our models ({BigGANs}) achieve an Inception Score ({IS}) of 166.5 and Frechet Inception Distance ({FID}) of 7.4, improving over the previous best {IS} of 52.52 and {FID} of 18.6.},
	journaltitle = {International Conference on Learning Representations ({ICLR})},
	author = {Brock, Andrew and Donahue, Jeff and Simonyan, K.},
	urldate = {2024-05-15},
	date = {2018-09-27},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/PPW5BDQB/Brock et al. - 2018 - Large Scale GAN Training for High Fidelity Natural.pdf:application/pdf},
}

@inproceedings{zhang_adversarial_2017,
	location = {Sydney, {NSW}, Australia},
	title = {Adversarial feature matching for text generation},
	series = {{ICML}'17},
	abstract = {The Generative Adversarial Network ({GAN}) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of {GAN} to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a con-volutional network as discriminator. Instead of using the standard objective of {GAN}, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.},
	pages = {4006--4015},
	booktitle = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},
	publisher = {{JMLR}.org},
	author = {Zhang, Yizhe and Gan, Zhe and Fan, Kai and Chen, Zhi and Henao, Ricardo and Shen, Dinghan and Carin, Lawrence},
	urldate = {2024-05-15},
	date = {2017-08-06},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/9RZM57HL/Zhang et al. - 2017 - Adversarial feature matching for text generation.pdf:application/pdf},
}

@inproceedings{bache_text-based_2013,
	location = {New York, {NY}, {USA}},
	title = {Text-based measures of document diversity},
	isbn = {978-1-4503-2174-7},
	url = {https://doi.org/10.1145/2487575.2487672},
	doi = {10.1145/2487575.2487672},
	series = {{KDD} '13},
	abstract = {Quantitative notions of diversity have been explored across a variety of disciplines ranging from conservation biology to economics. However, there has been relatively little work on measuring the diversity of text documents via their content. In this paper we present a text-based framework for quantifying how diverse a document is in terms of its content. The proposed approach learns a topic model over a corpus of documents, and computes a distance matrix between pairs of topics using measures such as topic co-occurrence. These pairwise distance measures are then combined with the distribution of topics within a document to estimate each document's diversity relative to the rest of the corpus. The method provides several advantages over existing methods. It is fully data-driven, requiring only the text from a corpus of documents as input, it produces human-readable explanations, and it can be generalized to score diversity of other entities such as authors, academic departments, or journals. We describe experimental results on several large data sets which suggest that the approach is effective and accurate in quantifying how diverse a document is relative to other documents in a corpus.},
	pages = {23--31},
	booktitle = {Proceedings of the 19th {ACM} {SIGKDD} international conference on Knowledge discovery and data mining},
	publisher = {Association for Computing Machinery},
	author = {Bache, Kevin and Newman, David and Smyth, Padhraic},
	urldate = {2024-05-15},
	date = {2013-08-11},
	keywords = {diversity, interdisciplinarity},
}

@inproceedings{alihosseini_jointly_2019,
	location = {Minneapolis, Minnesota},
	title = {Jointly Measuring Diversity and Quality in Text Generation Models},
	url = {https://aclanthology.org/W19-2311},
	doi = {10.18653/v1/W19-2311},
	abstract = {Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as {BLEU} only consider the quality of generated sentences and neglecting their diversity. For example, repeatedly generation of only one high quality sentence would result in a high {BLEU} score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-{BLEU} ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called {BERT} is introduced. Finally, for oracle training mode in which the generatorʼs density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.},
	eventtitle = {{NAACL} 2019},
	pages = {90--98},
	booktitle = {Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation},
	publisher = {Association for Computational Linguistics},
	author = {Alihosseini, Danial and Montahaei, Ehsan and Soleymani Baghshah, Mahdieh},
	editor = {Bosselut, Antoine and Celikyilmaz, Asli and Ghazvininejad, Marjan and Iyer, Srinivasan and Khandelwal, Urvashi and Rashkin, Hannah and Wolf, Thomas},
	urldate = {2024-05-15},
	date = {2019-06},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/E3ZVVPWB/Alihosseini et al. - 2019 - Jointly Measuring Diversity and Quality in Text Ge.pdf:application/pdf},
}

@misc{leinster_entropy_2022,
	title = {Entropy and Diversity: The Axiomatic Approach},
	url = {http://arxiv.org/abs/2012.02113},
	doi = {10.48550/arXiv.2012.02113},
	shorttitle = {Entropy and Diversity},
	abstract = {This book brings new mathematical rigour to the ongoing vigorous debate on how to quantify biological diversity. The question "what is diversity?" has surprising mathematical depth, and breadth too: this book involves parts of mathematics ranging from information theory, functional equations and probability theory to category theory, geometric measure theory and number theory. It applies the power of the axiomatic method to a biological problem of pressing concern, but the new concepts and theorems are also motivated from a purely mathematical perspective. The main narrative thread requires no more than an undergraduate course in analysis. No familiarity with entropy or diversity is assumed.},
	number = {{arXiv}:2012.02113},
	publisher = {{arXiv}},
	author = {Leinster, Tom},
	urldate = {2024-05-15},
	date = {2022-10-22},
	eprinttype = {arxiv},
	eprint = {2012.02113 [cs, math, q-bio]},
	keywords = {92B99, 94A17, 39B99, 26E60, 18D50, Computer Science - Information Theory, Mathematics - Category Theory, Mathematics - Classical Analysis and {ODEs}, Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods},
	file = {arXiv Fulltext PDF:/Users/azimospanov/Zotero/storage/JIILIRCB/Leinster - 2022 - Entropy and Diversity The Axiomatic Approach.pdf:application/pdf;arXiv.org Snapshot:/Users/azimospanov/Zotero/storage/W5WRQ2L6/2012.html:text/html},
}

@inproceedings{bhattarai_measuring_2021,
	title = {Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier},
	url = {http://arxiv.org/abs/2011.08755},
	doi = {10.5220/0010382204100417},
	abstract = {Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they are challenging to interpret due to their black-box nature. This paper addresses {\textbackslash}emph\{interpretable\} open-world text classification, where the trained classifier must deal with novel classes during operation. To this end, we extend the recently introduced Tsetlin machine ({TM}) with a novelty scoring mechanism. The mechanism uses the conjunctive clauses of the {TM} to measure to what degree a text matches the classes covered by the training data. We demonstrate that the clauses provide a succinct interpretable description of known topics, and that our scoring mechanism makes it possible to discern novel topics from the known ones. Empirically, our {TM}-based approach outperforms seven other novelty detection schemes on three out of five datasets, and performs second and third best on the remaining, with the added benefit of an interpretable propositional logic-based representation.},
	eventtitle = {13th International Conference on Agents and Artificial Intelligence},
	publisher = {{arXiv}},
	author = {Bhattarai, Bimal and Granmo, Ole-Christoffer and Jiao, Lei},
	urldate = {2024-05-15},
	date = {2021-01},
	eprinttype = {arxiv},
	eprint = {2011.08755 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, I.2, I.5, I.7},
	file = {arXiv Fulltext PDF:/Users/azimospanov/Zotero/storage/WIY2TKRH/Bhattarai et al. - 2020 - Measuring the Novelty of Natural Language Text Usi.pdf:application/pdf;arXiv.org Snapshot:/Users/azimospanov/Zotero/storage/4Y8HU886/2011.html:text/html},
}

@article{kumar_semantic_2020,
	title = {Semantic similarity and text summarization based novelty detection},
	volume = {2},
	issn = {2523-3971},
	url = {https://doi.org/10.1007/s42452-020-2082-z},
	doi = {10.1007/s42452-020-2082-z},
	abstract = {Current web crawlers search the queries at very high speed, but the problem of novelty detection or redundant information still persists. It consumes precious time and memory of users in search of the new document over the internet. In this paper, an innovative novelty detection mechanism is proposed, which can be appended with the current web crawlers. The proposed mechanism first summarizes the text, based on ontology, and from the obtained summary, semantic similarity is calculated using word net 3.0. The hash value is then calculated using the winnowing algorithm. This hash value of the document is matched with others using the Dice coefficient to calculate the similarity index. Based on the threshold chosen for similarity, the document is treated either as novel or not. This proposed mechanism is implemented using {SQL} as backend and visual studio-2012 as frontend. The results show that the projected strategy not only reduces memory consumption but also decreases the number of resultant documents, hence minimize the user time for searching the data from the obtained results. Also, the proposed approach can be used with other search engines like Google, Yahoo, Bing, and Alta Vista to minimize superfluous documents.},
	pages = {332},
	number = {3},
	journaltitle = {{SN} Applied Sciences},
	shortjournal = {{SN} Appl. Sci.},
	author = {Kumar, Sushil and Bhatia, Komal Kumar},
	urldate = {2024-05-15},
	date = {2020-02-04},
	langid = {english},
	keywords = {Dice coefficient, Hash value, Novelty detection, Semantic similarity, Word net 3.0},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/SYT36MTU/Kumar and Bhatia - 2020 - Semantic similarity and text summarization based n.pdf:application/pdf},
}


@inproceedings{ghashami2016streaming,
  title={Streaming kernel principal component analysis},
  author={Ghashami, Mina and Perry, Daniel J and Phillips, Jeff},
  booktitle={Artificial intelligence and statistics},
  pages={1365--1374},
  year={2016},
  organization={PMLR}
}

@incollection{hoffman2003variation,
  title={The variation of the spectrum of a normal matrix},
  author={Hoffman, Alan J and Wielandt, Helmut W},
  booktitle={Selected Papers Of Alan J Hoffman: With Commentary},
  pages={118--120},
  year={2003},
  publisher={World Scientific}
}


@article{ullah2018streaming,
  title={Streaming Kernel PCA with $O(\sqrt{n})$ Random Features},
  author={Ullah, Enayat and Mianjy, Poorya and Marinov, Teodor Vanislavov and Arora, Raman},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}

@article{sriperumbudur2022approximate,
  title={Approximate kernel pca: Computational versus statistical trade-off},
  author={Sriperumbudur, Bharath K and Sterge, Nicholas},
  journal={The Annals of Statistics},
  volume={50},
  number={5},
  pages={2713--2736},
  year={2022},
  publisher={Institute of Mathematical Statistics}
}

@inproceedings{chitta2012efficient,
  title={Efficient kernel clustering using random fourier features},
  author={Chitta, Radha and Jin, Rong and Jain, Anil K},
  booktitle={2012 IEEE 12th International Conference on Data Mining},
  pages={161--170},
  year={2012},
  organization={IEEE}
}

@article{gedon2023invertible,
  title={Invertible kernel pca with random fourier features},
  author={Gedon, Daniel and Ribeiro, Ant{\^o}nio H and Wahlstr{\"o}m, Niklas and Sch{\"o}n, Thomas B},
  journal={IEEE Signal Processing Letters},
  volume={30},
  pages={563--567},
  year={2023},
  publisher={IEEE}
}

@book{bengio2003spectral,
  title={Spectral clustering and kernel PCA are learning eigenfunctions},
  author={Bengio, Yoshua and Vincent, Pascal and Paiement, Jean-Fran{\c{c}}ois and Delalleau, Olivier and Ouimet, Marie and Le Roux, Nicolas},
  volume={1239},
  year={2003},
  publisher={Citeseer}
}

@techreport{bengio2003learning,
  title={Learning eigenfunctions of similarity: linking spectral clustering and kernel PCA},
  author={Bengio, Yoshua and Vincent, Pascal and Paiement, Jean-Fran{\c{c}}ois and Delalleau, O and Ouimet, M and LeRoux, N},
  year={2003},
  institution={Technical Report 1232, Departement d’Informatique et Recherche Oprationnelle~…}
}

@inproceedings{pasarkar2023cousins,
  title={Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning},
  author={Pasarkar, Amey and Dieng, Adji Bousso},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2024},
  organization={PMLR}
}

@inproceedings{li2023mode,
  title={Mode-seeking divergences: theory and applications to GANs},
  author={Li, Cheuk Ting and Farnia, Farzan},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={8321--8350},
  year={2023},
  organization={PMLR}
}

@inproceedings{basu_evaluating_2001,
	location = {New York, {NY}, {USA}},
	title = {Evaluating the novelty of text-mined rules using lexical knowledge},
	isbn = {978-1-58113-391-2},
	url = {https://doi.org/10.1145/502512.502544},
	doi = {10.1145/502512.502544},
	series = {{KDD} '01},
	abstract = {In this paper, we present a new method of estimating the novelty of rules discovered by data-mining methods using {WordNet}, a lexical knowledge-base of English words. We assess the novelty of a rule by the average semantic distance in a knowledge hierarchy between the words in the antecedent and the consequent of the rule - the more the average distance, more is the novelty of the rule. The novelty of rules extracted by the {DiscoTEX} text-mining system on Amazon.com book descriptions were evaluated by both human subjects and by our algorithm. By computing correlation coefficients between pairs of human ratings and between human and automatic ratings, we found that the automatic scoring of rules based on our novelty measure correlates with human judgments about as well as human judgments correlate with one another. @Text mining},
	pages = {233--238},
	booktitle = {Proceedings of the seventh {ACM} {SIGKDD} international conference on Knowledge discovery and data mining},
	publisher = {Association for Computing Machinery},
	author = {Basu, Sugato and Mooney, Raymond J. and Pasupuleti, Krupakar V. and Ghosh, Joydeep},
	urldate = {2024-05-15},
	date = {2001-08-26},
	keywords = {interesting rules, knowledge hierarchy, novelty, semantic distance, {WordNet}},
}

@inproceedings{goodfellow_generative_2014,
	title = {Generative Adversarial Nets},
	volume = {27},
	url = {https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html},
    year={2014},
	abstract = {We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitatively evaluation of the generated samples.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
	urldate = {2024-05-15},
	date = {2014},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/WDIC37CV/Goodfellow et al. - 2014 - Generative Adversarial Nets.pdf:application/pdf},
}

@inproceedings{oquab_dinov2_2023,
	title = {{DINOv}2: Learning Robust Visual Features without Supervision},
	issn = {2835-8856},
    year={2023},
	url = {https://openreview.net/forum?id=a68SUt6zFt},
	shorttitle = {{DINOv}2},
	abstract = {The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a {ViT} model with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, {OpenCLIP} on most of the benchmarks at image and pixel levels.},
	booktitle = {Transactions on Machine Learning Research},
	author = {Oquab, Maxime and Darcet, Timothée and Moutakanni, Théo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Assran, Mido and Ballas, Nicolas and Galuba, Wojciech and Howes, Russell and Huang, Po-Yao and Li, Shang-Wen and Misra, Ishan and Rabbat, Michael and Sharma, Vasu and Synnaeve, Gabriel and Xu, Hu and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
	urldate = {2024-05-15},
	date = {2023-07-26},
	langid = {english},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/PEHSE54F/Oquab et al. - 2023 - DINOv2 Learning Robust Visual Features without Su.pdf:application/pdf},
}

@inproceedings{campos_yake_2020,
	title = {{YAKE}! Keyword extraction from single documents using multiple local features},
	volume = {509},
    year={2020},
	issn = {0020-0255},
	url = {https://www.sciencedirect.com/science/article/pii/S0020025519308588},
	doi = {10.1016/j.ins.2019.09.013},
	abstract = {As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe {YAKE}!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of {YAKE}!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that {YAKE}! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains.},
	pages = {257--289},
	booktitle = {Information Sciences},
	shortjournal = {Information Sciences},
	author = {Campos, Ricardo and Mangaravite, Vítor and Pasquali, Arian and Jorge, Alípio and Nunes, Célia and Jatowt, Adam},
	urldate = {2024-05-15},
	date = {2020-01-01},
	keywords = {Information extraction, Keyword extraction, Unsupervised Algorithm},
	file = {ScienceDirect Snapshot:/Users/azimospanov/Zotero/storage/J9F85RGS/S0020025519308588.html:text/html},
}

@inproceedings{kingma2013auto,
	title = {Auto-Encoding Variational Bayes},
	url = {https://openreview.net/forum?id=33X9fd2-9FyZd},
    year={2013},
	abstract = {Can we efficiently learn the parameters of directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions? We introduce an unsupervised on-line learning method that efficiently optimizes the variational lower bound on the marginal likelihood and that, under some mild conditions, even works in the intractable case. The method optimizes a probabilistic encoder (also called a recognition network) to approximate the intractable posterior distribution of the latent variables. The crucial element is a reparameterization of the variational bound with an independent noise variable, yielding a stochastic objective function which can be jointly optimized w.r.t. variational and generative parameters using standard gradient-based stochastic optimization methods. Theoretical advantages are reflected in experimental results.},
	author = {Kingma, Diederik P. and Welling, Max},
	urldate = {2024-05-15},
    booktitle = {International Conference on Learning Representations (ICLR)},
	date = {2013-12-23},
	langid = {english},
}

@inproceedings{binkowski2018demystifying,
  title={Demystifying MMD GANs},
  author={Bi{\'n}kowski, Miko{\l}aj and Sutherland, Danica J and Arbel, Michael and Gretton, Arthur},
  booktitle={International Conference on Learning Representations},
  year={2018}
}


@inproceedings{fleet_microsoft_2014,
	location = {Cham},
	title = {Microsoft {COCO}: Common Objects in Context},
	volume = {8693},
    year={2014},
	rights = {http://www.springer.com/tdm},
	isbn = {978-3-319-10601-4 978-3-319-10602-1},
	url = {http://link.springer.com/10.1007/978-3-319-10602-1_48},
	doi = {10.1007/978-3-319-10602-1_48},
	shorttitle = {Microsoft {COCO}},
	abstract = {We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to {PASCAL}, {ImageNet}, and {SUN}. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.},
	pages = {740--755},
	booktitle = {European Conference on Computer Vision ({ECCV})},
	publisher = {Springer International Publishing},
	author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C. Lawrence},
	editor = {Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne},
	urldate = {2024-05-15},
	date = {2014},
	langid = {english},
	doi = {10.1007/978-3-319-10602-1_48},
	note = {Book Title: Computer Vision – {ECCV} 2014
Series Title: Lecture Notes in Computer Science},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/RZVEKMC6/Lin et al. - 2014 - Microsoft COCO Common Objects in Context.pdf:application/pdf},
}

@inproceedings{deng_mnist_2012,
	title = {The {MNIST} Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]},
	volume = {29},
	issn = {1558-0792},
    year={2012},
	url = {https://ieeexplore.ieee.org/document/6296535},
	doi = {10.1109/MSP.2012.2211477},
	abstract = {In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology ({MNIST}) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.},
	pages = {141--142},
	number = {6},
	booktitle = {{IEEE} Signal Processing Magazine},
	author = {Deng, Li},
	urldate = {2024-05-15},
	date = {2012-11},
	note = {Conference Name: {IEEE} Signal Processing Magazine},
	keywords = {Machine learning},
	file = {IEEE Xplore Abstract Record:/Users/azimospanov/Zotero/storage/3GFDLQ5V/6296535.html:text/html},
}

@online{xiao2017/online,
  author       = {Han Xiao and Kashif Rasul and Roland Vollgraf},
  title        = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
  date         = {2017-08-28},
  year         = {2017},
  eprintclass  = {cs.LG},
  eprinttype   = {arXiv},
  eprint       = {cs.LG/1708.07747},
}

@inproceedings{stein_exposing_2023,
	title = {Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models},
	volume = {36},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Stein, George and Cresswell, Jesse and Hosseinzadeh, Rasa and Sui, Yi and Ross, Brendan and Villecroze, Valentin and Liu, Zhaoyan and Caterini, Anthony L and Taylor, Eric and Loaiza-Ganem, Gabriel},
	editor = {Oh, A. and Naumann, T. and Globerson, A. and Saenko, K. and Hardt, M. and Levine, S.},
	year = {2023},
	pages = {3732--3784},
}

@inproceedings{kynkaanniemi_role_2022,
	title = {The {Role} of {ImageNet} {Classes} in {Fréchet} {Inception} {Distance}},
	url = {https://openreview.net/forum?id=4oXTQ6m_ws8},
	abstract = {Fréchet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling. While remarkably successful, the metric is known to sometimes disagree with human judgement. We investigate a root cause of these discrepancies, and visualize what FID "looks at" in generated images. We show that the feature space that FID is (typically) computed in is so close to the ImageNet classifications that aligning the histograms of Top-\$N\$ classifications between sets of generated and real images can reduce FID substantially — without actually improving the quality of results. Thus, we conclude that FID is prone to intentional or accidental distortions. As a practical example of an accidental distortion, we discuss a case where an ImageNet pre-trained FastGAN achieves a FID comparable to StyleGAN2, while being worse in terms of human evaluation.},
	language = {en},
	urldate = {2024-05-18},
	author = {Kynkäänniemi, Tuomas and Karras, Tero and Aittala, Miika and Aila, Timo and Lehtinen, Jaakko},
	month = sep,
	year = {2022},
	file = {Full Text PDF:C\:\\Users\\admin\\Zotero\\storage\\35P92WRQ\\Kynkäänniemi et al. - 2022 - The Role of ImageNet Classes in Fréchet Inception .pdf:application/pdf},
}


@misc{padmakumar_does_2024,
	title = {Does {Writing} with {Language} {Models} {Reduce} {Content} {Diversity}?},
	url = {http://arxiv.org/abs/2309.05196},
	doi = {10.48550/arXiv.2309.05196},
	abstract = {Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of decreased diversity in the produced content, potentially limiting diverse perspectives in public discourse. In this work, we measure the impact of co-writing on diversity via a controlled experiment, where users write argumentative essays in three setups -- using a base LLM (GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity. Specifically, it increases the similarity between the writings of different authors and reduces the overall lexical and content diversity. We additionally find that this effect is mainly attributable to InstructGPT contributing less diverse text to co-written essays. In contrast, the user-contributed text remains unaffected by model collaboration. This suggests that the recent improvement in generation quality from adapting models to human feedback might come at the cost of more homogeneous and less diverse content.},
	urldate = {2024-05-18},
	publisher = {arXiv},
	author = {Padmakumar, Vishakh and He, He},
	month = mar,
	year = {2024},
	note = {arXiv:2309.05196 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning},
	annote = {Comment: ICLR 2024},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\GLYNC74Z\\Padmakumar and He - 2024 - Does Writing with Language Models Reduce Content D.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\V38Q3MZ5\\2309.html:text/html},
}

@article{meister_locally_2023,
	title = {Locally {Typical} {Sampling}},
	volume = {11},
	url = {https://aclanthology.org/2023.tacl-1.7},
	doi = {10.1162/tacl_a_00536},
	abstract = {Today's probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics (e.g., perplexity). This discrepancy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language generation as a discrete stochastic process—which allows for an information-theoretic analysis—can provide new insights into the behavior of probabilistic language generators, for example, why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind. We formally define the set of strings that meet this criterion: Those for which each word has an information content close to the expected information content, namely, the conditional entropy of our model. We then propose a simple and efficient procedure for enforcing this criterion when generating from probabilistic models, which we call locally typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions.},
	urldate = {2024-05-18},
	journal = {Transactions of the Association for Computational Linguistics},
	author = {Meister, Clara and Pimentel, Tiago and Wiher, Gian and Cotterell, Ryan},
	year = {2023},
	note = {Place: Cambridge, MA
Publisher: MIT Press},
	pages = {102--121},
	file = {Full Text PDF:C\:\\Users\\admin\\Zotero\\storage\\KUDMMXEU\\Meister et al. - 2023 - Locally Typical Sampling.pdf:application/pdf},
}

@inproceedings{li_contrastive_2023,
	address = {Toronto, Canada},
	title = {Contrastive {Decoding}: {Open}-ended {Text} {Generation} as {Optimization}},
	shorttitle = {Contrastive {Decoding}},
	url = {https://aclanthology.org/2023.acl-long.687},
	doi = {10.18653/v1/2023.acl-long.687},
	abstract = {Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts from the original topics. We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. The contrastive objective returns the difference between the likelihood under a large LM (called the expert, e.g. OPT-13B) and a small LM (called the amateur, e.g. OPT-125M), and the constraint ensures that the outputs are plausible. CD is inspired by the fact that the failures of larger LMs (e.g., repetition, inco- herence) are even more prevalent in smaller LMs, and that this difference signals which texts should be preferred. CD requires zero additional training, and produces higher quality text than decoding from the larger LM alone. It also works across model scales (OPT-13B and GPT2-1.5B) and significantly outperforms four strong decoding algorithms (e.g., nucleus, top-k) in automatic and human evaluations across wikipedia, news and story domains.},
	urldate = {2024-05-18},
	booktitle = {Proceedings of the 61st {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Li, Xiang Lisa and Holtzman, Ari and Fried, Daniel and Liang, Percy and Eisner, Jason and Hashimoto, Tatsunori and Zettlemoyer, Luke and Lewis, Mike},
	editor = {Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki},
	month = jul,
	year = {2023},
	pages = {12286--12312},
	file = {Full Text PDF:C\:\\Users\\admin\\Zotero\\storage\\I5QA5FE8\\Li et al. - 2023 - Contrastive Decoding Open-ended Text Generation a.pdf:application/pdf},
}


@inproceedings{lin_automatic_2004,
	address = {Barcelona, Spain},
	title = {Automatic {Evaluation} of {Machine} {Translation} {Quality} {Using} {Longest} {Common} {Subsequence} and {Skip}-{Bigram} {Statistics}},
	url = {https://aclanthology.org/P04-1077},
	doi = {10.3115/1218955.1219032},
	urldate = {2024-05-18},
	booktitle = {Proceedings of the 42nd {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({ACL}-04)},
	author = {Lin, Chin-Yew and Och, Franz Josef},
	month = jul,
	year = {2004},
	pages = {605--612},
	file = {Full Text PDF:C\:\\Users\\admin\\Zotero\\storage\\DXJJ92UT\\Lin and Och - 2004 - Automatic Evaluation of Machine Translation Qualit.pdf:application/pdf},
}


@inproceedings{montahaei_jointly_2019,
	title = {Jointly {Measuring} {Diversity} and {Quality} in {Text} {Generation} {Models}},
	url = {http://arxiv.org/abs/1904.03971},
	doi = {10.48550/arXiv.1904.03971},
	abstract = {Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generator's density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.},
	urldate = {2024-05-18},
	publisher = {arXiv},
	author = {Montahaei, Ehsan and Alihosseini, Danial and Baghshah, Mahdieh Soleymani},
	month = may,
	year = {2019},
	note = {arXiv:1904.03971 [cs, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Accepted by NAACL 2019 workshop (NeuralGen 2019)},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\WH9PART6\\Montahaei et al. - 2019 - Jointly Measuring Diversity and Quality in Text Ge.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\U3UTHWUT\\1904.html:text/html},
}


@inproceedings{caron_unsupervised_2020,
	title = {Unsupervised {Learning} of {Visual} {Features} by {Contrasting} {Cluster} {Assignments}},
	volume = {33},
	url = {https://proceedings.neurips.cc/paper/2020/hash/70feb62b69f16e0238f741fab228fec2-Abstract.html},
	abstract = {Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the code of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements. We validate our findings by achieving 75.3\% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.},
	urldate = {2024-05-18},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand},
	year = {2020},
	pages = {9912--9924},
	file = {Full Text PDF:C\:\\Users\\admin\\Zotero\\storage\\TC3N8E78\\Caron et al. - 2020 - Unsupervised Learning of Visual Features by Contra.pdf:application/pdf},
}


@inproceedings{radford_learning_2021,
	title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
	url = {http://arxiv.org/abs/2103.00020},
	doi = {10.48550/arXiv.2103.00020},
	abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
	urldate = {2024-05-18},
	booktitle = {International {Conference} on {Machine} {Learning}},
	publisher = {arXiv},
	author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
	month = feb,
	year = {2021},
	note = {arXiv:2103.00020 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	pages = {8748--8763},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\GD9UFAGN\\Radford et al. - 2021 - Learning Transferable Visual Models From Natural L.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\7EMF4SK4\\2103.html:text/html},
}


@inproceedings{sutherland_error_2015,
	title = {On the {Error} of {Random} {Fourier} {Features}},
	url = {http://arxiv.org/abs/1506.02785},
	doi = {10.48550/arXiv.1506.02785},
	abstract = {Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning. The standard approach, however, requires pairwise evaluations of a kernel function, which can lead to scalability issues for very large datasets. Rahimi and Recht (2007) suggested a popular approach to handling this problem, known as random Fourier features. The quality of this approximation, however, is not well understood. We improve the uniform error bound of that paper, as well as giving novel understandings of the embedding's variance, approximation error, and use in some machine learning methods. We also point out that surprisingly, of the two main variants of those features, the more widely used is strictly higher-variance for the Gaussian kernel and has worse bounds.},
	urldate = {2024-05-18},
	booktitle = {Uncertainty in {Artificial} {Intelligence} ({UAI}) 2015},
	publisher = {arXiv},
	author = {Sutherland, Danica J. and Schneider, Jeff},
	month = jun,
	year = {2015},
	note = {arXiv:1506.02785 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Published at UAI 2015},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\ZFI7AV4R\\Sutherland and Schneider - 2015 - On the Error of Random Fourier Features.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\39HEH7JK\\1506.html:text/html},
}


@article{scholkopf_nonlinear_1998,
	title = {Nonlinear {Component} {Analysis} as a {Kernel} {Eigenvalue} {Problem}},
	volume = {10},
	issn = {0899-7667},
	url = {https://ieeexplore.ieee.org/document/6790375},
	doi = {10.1162/089976698300017467},
	abstract = {A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.},
	number = {5},
	urldate = {2024-05-18},
	journal = {Neural Computation},
	author = {Schölkopf, Bernhard and Smola, Alexander and Müller, Klaus-Robert},
	month = jul,
	year = {1998},
	note = {Conference Name: Neural Computation},
	pages = {1299--1319},
}


@misc{alaa_how_2022,
	title = {How {Faithful} is your {Synthetic} {Data}? {Sample}-level {Metrics} for {Evaluating} and {Auditing} {Generative} {Models}},
	shorttitle = {How {Faithful} is your {Synthetic} {Data}?},
	url = {http://arxiv.org/abs/2102.08921},
	doi = {10.48550/arXiv.2102.08921},
	abstract = {Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a limited capacity for diagnosing the different modes of failure of generative models across broader application domains. In this paper, we introduce a 3-dimensional evaluation metric, (\${\textbackslash}alpha\$-Precision, \${\textbackslash}beta\$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies statistical divergence measures with precision-recall analysis, enabling sample- and distribution-level diagnoses of model fidelity and diversity. We introduce generalization as an additional, independent dimension (to the fidelity-diversity trade-off) that quantifies the extent to which a model copies training data -- a crucial performance indicator when modeling sensitive data with requirements on privacy. The three metric components correspond to (interpretable) probabilistic quantities, and are estimated via sample-level binary classification. The sample-level nature of our metric inspires a novel use case which we call model auditing, wherein we judge the quality of individual samples generated by a (black-box) model, discarding low-quality samples and hence improving the overall model performance in a post-hoc manner.},
	urldate = {2024-05-19},
	publisher = {arXiv},
	author = {Alaa, Ahmed M. and van Breugel, Boris and Saveliev, Evgeny and van der Schaar, Mihaela},
	month = jul,
	year = {2022},
	note = {arXiv:2102.08921 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\8Z33ZB2E\\Alaa et al. - 2022 - How Faithful is your Synthetic Data Sample-level .pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\ZATUQBR2\\2102.html:text/html},
}

@inproceedings{choi2020starganv2,
  title={StarGAN v2: Diverse Image Synthesis for Multiple Domains},
  author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}


@misc{openai_gpt-4_2024,
	title = {{GPT}-4 {Technical} {Report}},
	url = {http://arxiv.org/abs/2303.08774},
	doi = {10.48550/arXiv.2303.08774},
	abstract = {We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10\% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.},
	urldate = {2024-05-19},
	publisher = {arXiv},
	author = {OpenAI},
	month = mar,
	year = {2024},
	note = {arXiv:2303.08774 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
	annote = {Comment: 100 pages; updated authors list; fixed author names and added citation},
	file = {arXiv Fulltext PDF:C\:\\Users\\admin\\Zotero\\storage\\9PRF6GXE\\OpenAI et al. - 2024 - GPT-4 Technical Report.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\admin\\Zotero\\storage\\QKYZKGYW\\2303.html:text/html},
}

@misc{openai2023textembedding,
  author       = {OpenAI},
  title        = {text-embedding-3-large},
  year         = {2024},
  howpublished = {\url{https://platform.openai.com/docs/models/embeddings}}
}

@inproceedings{cmu2013,
    author = {David Bamman, Brendan O'Connor, and Noah A. Smith},
    title = {Learning Latent Personas of Film Characters},
    booktitle = {ACL 2013},
    year = {2013}
}

@inproceedings{see-etal-2017-get,
    title = "Get To The Point: Summarization with Pointer-Generator Networks",
    author = "See, Abigail  and
      Liu, Peter J.  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1099",
    doi = "10.18653/v1/P17-1099",
    pages = "1073--1083",
    abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}

@inproceedings{HermannKGEKSB15,
  author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
  title={Teaching Machines to Read and Comprehend},
  year={2015},
  cdate={1420070400000},
  pages={1693-1701},
  url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
  booktitle={NIPS},
  crossref={conf/nips/2015}
}

@misc{soomro2012ucf101,
      title={UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild}, 
      author={Khurram Soomro and Amir Roshan Zamir and Mubarak Shah},
      year={2012},
      eprint={1212.0402},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{kay2017kinetics,
      title={The Kinetics Human Action Video Dataset}, 
      author={Will Kay and Joao Carreira and Karen Simonyan and Brian Zhang and Chloe Hillier and Sudheendra Vijayanarasimhan and Fabio Viola and Tim Green and Trevor Back and Paul Natsev and Mustafa Suleyman and Andrew Zisserman},
      year={2017},
      eprint={1705.06950},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@INPROCEEDINGS{Carreira_i3d,
  author={Carreira, João and Zisserman, Andrew},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset}, 
  year={2017},
  volume={},
  number={},
  pages={4724-4733},
  keywords={Videos;Three-dimensional displays;Kinetic theory;Two dimensional displays;Kernel;Feature extraction;Solid modeling},
  doi={10.1109/CVPR.2017.502}
}

@Article{Saito2020,
author={Saito, Masaki
and Saito, Shunta
and Koyama, Masanori
and Kobayashi, Sosuke},
title={Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN},
journal={International Journal of Computer Vision},
year={2020},
month={Nov},
day={01},
volume={128},
number={10},
pages={2586-2606},
issn={1573-1405},
doi={10.1007/s11263-020-01333-y},
}

@misc{unterthiner2019accurate,
      title={Towards Accurate Generative Models of Video: A New Metric & Challenges}, 
      author={Thomas Unterthiner and Sjoerd van Steenkiste and Karol Kurach and Raphael Marinier and Marcin Michalski and Sylvain Gelly},
      year={2019},
      eprint={1812.01717},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@inproceedings{dhariwal_diffusion_2021,
	title = {Diffusion Models Beat {GANs} on Image Synthesis},
	volume = {34},
	url = {https://papers.nips.cc/paper/2021/hash/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html},
	pages = {8780--8794},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Dhariwal, Prafulla and Nichol, Alexander},
	urldate = {2024-05-22},
	date = {2021},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/DPZ5RVLZ/Dhariwal and Nichol - 2021 - Diffusion Models Beat GANs on Image Synthesis.pdf:application/pdf},
}


@misc{peebles_scalable_2023,
	title = {Scalable Diffusion Models with Transformers},
	url = {http://arxiv.org/abs/2212.09748},
	doi = {10.48550/arXiv.2212.09748},
	abstract = {We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers ({DiTs}) through the lens of forward pass complexity as measured by Gflops. We find that {DiTs} with higher Gflops -- through increased transformer depth/width or increased number of input tokens -- consistently have lower {FID}. In addition to possessing good scalability properties, our largest {DiT}-{XL}/2 models outperform all prior diffusion models on the class-conditional {ImageNet} 512x512 and 256x256 benchmarks, achieving a state-of-the-art {FID} of 2.27 on the latter.},
	number = {{arXiv}:2212.09748},
	publisher = {{arXiv}},
	author = {Peebles, William and Xie, Saining},
	urldate = {2024-05-22},
	date = {2023-03-02},
	eprinttype = {arxiv},
	eprint = {2212.09748 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {arXiv Fulltext PDF:/Users/azimospanov/Zotero/storage/4GQBVIBP/Peebles and Xie - 2023 - Scalable Diffusion Models with Transformers.pdf:application/pdf;arXiv.org Snapshot:/Users/azimospanov/Zotero/storage/MZL8IPK5/2212.html:text/html},
}

@inproceedings{kang2023gigagan,
  author    = {Kang, Minguk and Zhu, Jun-Yan and Zhang, Richard and Park, Jaesik and Shechtman, Eli and Paris, Sylvain and Park, Taesung},
  title     = {Scaling up GANs for Text-to-Image Synthesis},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2023},
}


@inproceedings{rombach_high-resolution_2022,
	title = {High-Resolution Image Synthesis with Latent Diffusion Models},
	isbn = {978-1-66546-946-3},
	url = {https://www.computer.org/csdl/proceedings-article/cvpr/2022/694600k0674/1H1iFsO7Zuw},
	doi = {10.1109/CVPR52688.2022.01042},
	abstract = {By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models ({DMs}) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful {DMs} often consumes hundreds of {GPU} days and inference is expensive due to sequential evaluations. To enable {DM} training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models ({LDMs}) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based {DMs}.},
	eventtitle = {2022 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition ({CVPR})},
	pages = {10674--10685},
	booktitle = {Conference on Computer Vision and Pattern Recognition ({CVPR})},
	publisher = {{IEEE} Computer Society},
	author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Björn},
	urldate = {2024-05-22},
	date = {2022-06-01},
	file = {Submitted Version:/Users/azimospanov/Zotero/storage/6S8LYQQW/Rombach et al. - 2022 - High-Resolution Image Synthesis with Latent Diffus.pdf:application/pdf},
}

@InProceedings{chang2022maskgit,
  title = {MaskGIT: Masked Generative Image Transformer},
  author={Huiwen Chang and Han Zhang and Lu Jiang and Ce Liu and William T. Freeman},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2022}
}

@inproceedings{lee2022autoregressive,
  title={Autoregressive Image Generation using Residual Quantization},
  author={Lee, Doyup and Kim, Chiheon and Kim, Saehoon and Cho, Minsu and Han, Wook-Shin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11523--11532},
  year={2022}
}

@InProceedings{Sauer2021ARXIV,
  author    = {Axel Sauer and Katja Schwarz and Andreas Geiger},
  title     = {StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets},
  booktitle   = {ACM SIGGRAPH 2022 Conference Proceedings},
  volume    = {abs/2201.00273},
  year      = {2022},
  url       = {https://arxiv.org/abs/2201.00273},
}


@inproceedings{yang_data-efficient_2021,
	title = {Data-Efficient Instance Generation from Instance Discrimination},
	volume = {34},
	url = {https://proceedings.neurips.cc/paper/2021/hash/4e0d67e54ad6626e957d15b08ae128a6-Abstract.html},
	pages = {9378--9390},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Yang, Ceyuan and Shen, Yujun and Xu, Yinghao and Zhou, Bolei},
	urldate = {2024-05-22},
	date = {2021},
	file = {Full Text PDF:/Users/azimospanov/Zotero/storage/G554FRF4/Yang et al. - 2021 - Data-Efficient Instance Generation from Instance D.pdf:application/pdf},
}


@inproceedings{child_very_2020,
	title = {Very Deep {VAEs} Generalize Autoregressive Models and Can Outperform Them on Images},
	url = {https://openreview.net/forum?id=RLRXCV6DbEJ},
	abstract = {We present a hierarchical {VAE} that, for the first time, generates samples quickly \${\textbackslash}textit\{and\}\$ outperforms the {PixelCNN} in log-likelihood on all natural image benchmarks. We begin by observing that, in theory, {VAEs} can actually represent autoregressive models, as well as faster, better models if they exist, when made sufficiently deep. Despite this, autoregressive models have historically outperformed {VAEs} in log-likelihood. We test if insufficient depth explains why by scaling a {VAE} to greater stochastic depth than previously explored and evaluating it {CIFAR}-10, {ImageNet}, and {FFHQ}. In comparison to the {PixelCNN}, these very deep {VAEs} achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. Qualitative studies suggest this is because the {VAE} learns efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae.},
	eventtitle = {International Conference on Learning Representations},
	author = {Child, Rewon},
	urldate = {2024-05-22},
	date = {2020-10-02},
	langid = {english},
}



@inproceedings{kohler2017sub,
  title={Sub-sampled cubic regularization for non-convex optimization},
  author={Kohler, Jonas Moritz and Lucchi, Aurelien},
  booktitle={International Conference on Machine Learning},
  pages={1895--1904},
  year={2017},
  organization={PMLR}
}

@article{gross2011recovering,
  title={Recovering low-rank matrices from few coefficients in any basis},
  author={Gross, David},
  journal={IEEE Transactions on Information Theory},
  volume={57},
  number={3},
  pages={1548--1566},
  year={2011},
  publisher={IEEE}
}


@inproceedings{xu2015nystrom,
  title={Nystrom approximation for sparse kernel methods: Theoretical analysis and empirical evaluation},
  author={Xu, Zenglin and Jin, Rong and Shen, Bin and Zhu, Shenghuo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={29},
  number={1},
  year={2015}
}