@inproceedings{
zhang2022ood,
title={{OOD} Detection with Class Ratio Estimation},
author={Mingtian Zhang and Andi Zhang and Tim Z. Xiao and Yitong Sun and Steven McDonagh},
booktitle={NeurIPS ML Safety Workshop},
year={2022},
}

@inproceedings{vulic-etal-2020-probing,
    title = "Probing Pretrained Language Models for Lexical Semantics",
    author = "Vuli{\'c}, Ivan  and
      Ponti, Edoardo Maria  and
      Litschko, Robert  and
      Glava{\v{s}}, Goran  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2020 Conference on EMNLP",
    month = nov,
    year = "2020",
    doi = "10.18653/v1/2020.emnlp-main.586",
    pages = "7222--7240",
    abstract = "The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture. While prior research focused on morphosyntactic, semantic, and world knowledge, it remains unclear to which extent LMs also derive lexical type-level knowledge from words in context. In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-of-context versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance? How consistent are the observed effects across tasks and languages? 2) Is lexical knowledge stored in few parameters, or is it scattered throughout the network? 3) How do these representations fare against traditional static word vectors in lexical tasks 4) Does the lexical information emerging from independently trained monolingual LMs display latent similarities? Our main results indicate patterns and best practices that hold universally, but also point to prominent variations across languages and tasks. Moreover, we validate the claim that lower Transformer layers carry more type-level lexical knowledge, but also show that this knowledge is distributed across multiple layers.",
}

@book{10.5555/1462129,
author = {Quionero-Candela, Joaquin and Sugiyama, Masashi and Schwaighofer, Anton and Lawrence, Neil D.},
title = {Dataset Shift in Machine Learning},
year = {2009},
isbn = {0262170051},
publisher = {The MIT Press},
abstract = {Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert Mller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schlkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural Information Processing series}
}



@inproceedings{
gaussian_process_uncertainty,
title={Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel},
author={Xin Qiu and Elliot Meyerson and Risto Miikkulainen},
booktitle={ICLR},
year={2020},
}

@inproceedings{
entropy_uncertainty,
title={Uncertainty Estimation in Autoregressive Structured Prediction},
author={Andrey Malinin and Mark Gales},
booktitle={ICLR},
year={2021},
}

@article{mse_uncertainty,
  author    = {Maria R. Cervera and
               Rafael D{\"{a}}twyler and
               Francesco D'Angelo and
               Hamza Keurti and
               Benjamin F. Grewe and
               Christian Henning},
  title     = {Uncertainty estimation under model misspecification in neural network
               regression},
  journal   = {CoRR},
  volume    = {abs/2111.11763},
  year      = {2021},
  eprinttype = {arXiv},
  eprint    = {2111.11763},
  timestamp = {Fri, 26 Nov 2021 13:48:43 +0100},
}

@article{hullermeier2021aleatoric,
  title={Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods},
  author={H{\"u}llermeier, Eyke and Waegeman, Willem},
  journal={Machine Learning},
  volume={110},
  number={3},
  pages={457--506},
  year={2021},
  publisher={Springer}
}

@article{bierens1988nadaraya,
  title={The nadaraya-watson kernel regression function estimator},
  author={Bierens, Hermanus Josephus},
  year={1988}
}

@article{Bang_Xie_Lee_Wu_Xing_2021, title={Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach}, volume={35},  DOI={10.1609/aaai.v35i13.17358}, abstractNote={Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiveness simultaneously, leading to redundant explanations. We propose the variational information bottleneck for interpretation, VIBI, a system-agnostic interpretable method that provides a brief but comprehensive explanation. VIBI adopts an information theoretic principle, information bottleneck principle, as a criterion for finding such explanations. For each instance, VIBI selects key features that are maximally compressed about an input (briefness), and informative about a decision made by a black-box system on that input (comprehensive). We evaluate VIBI on three datasets and compare with state-of-the-art interpretable machine learning methods in terms of both interpretability and fidelity evaluated by human and quantitative metrics.}, number={13}, journal={AAAI}, author={Bang, Seojin and Xie, Pengtao and Lee, Heewook and Wu, Wei and Xing, Eric}, year={2021}, month={May}, pages={11396-11404} }

@article{banerjee2005clustering,
  title={Clustering with Bregman divergences.},
  author={Banerjee, Arindam and Merugu, Srujana and Dhillon, Inderjit S and Ghosh, Joydeep and Lafferty, John},
  journal={Journal of machine learning research},
  volume={6},
  number={10},
  year={2005}
}

@inproceedings{zhang-etal-2021-orthogonality,
    title = "On Orthogonality Constraints for Transformers",
    author = "Zhang, Aston  and
      Chan, Alvin  and
      Tay, Yi  and
      Fu, Jie  and
      Wang, Shuohang  and
      Zhang, Shuai  and
      Shao, Huajie  and
      Yao, Shuochao  and
      Lee, Roy Ka-Wei",
    booktitle = "Proceedings of the 59th Annual Meeting of the ACL-IJCNLP",
    month = aug,
    year = "2021",
    doi = "10.18653/v1/2021.acl-short.48",
    pages = "375--382",
    abstract = "Orthogonality constraints encourage matrices to be orthogonal for numerical stability. These plug-and-play constraints, which can be conveniently incorporated into model training, have been studied for popular architectures in natural language processing, such as convolutional neural networks and recurrent neural networks. However, a dedicated study on such constraints for transformers has been absent. To fill this gap, this paper studies orthogonality constraints for transformers, showing the effectiveness with empirical evidence from ten machine translation tasks and two dialogue generation tasks. For example, on the large-scale WMT{'}16 En→De benchmark, simply plugging-and-playing orthogonality constraints on the original transformer model (Vaswani et al., 2017) increases the BLEU from 28.4 to 29.6, coming close to the 29.7 BLEU achieved by the very competitive dynamic convolution (Wu et al., 2019).",
}

@incollection{Bengio+chapter2007,
author = {Bengio, Yoshua and LeCun, Yann},
booktitle = {Large Scale Kernel Machines},
publisher = {MIT Press},
title = {Scaling Learning Algorithms Towards {AI}},
year = {2007}
}

@article{Hinton06,
author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
journal = {Neural Computation},
pages = {1527--1554},
title = {A Fast Learning Algorithm for Deep Belief Nets},
volume = {18},
year = {2006}
}

@book{goodfellow2016deep,
title={Deep learning},
author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
volume={1},
year={2016},
publisher={MIT Press}
}



% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {$L_1$}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th ICML},
  pages={33--40},
  year={2007}
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005},
	url={https://www.jmlr.org/papers/volume6/ando05a/ando05a.pdf}
}

@article{ct1965,
  title={An algorithm for the machine calculation of complex {F}ourier series},
  author={Cooley, James W. and Tukey, John W.},
  journal={Mathematics of Computation},
  volume={19},
  number={90},
  pages={297--301},
  year={1965},
  url={https://www.ams.org/journals/mcom/1965-19-090/S0025-5718-1965-0178586-1/S0025-5718-1965-0178586-1.pdf}
}







@inproceedings{bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the NACCL",
    month = jun,
    year = "2019",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).",
}

@misc{roberta,
title={Ro{BERT}a: A Robustly Optimized {BERT} Pretraining Approach},
author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
year={2020}
}

@inproceedings{albert,
title={ALBERT: A Lite BERT for Self-supervised Learning of Language Representations},
author={Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut},
booktitle={ICLR},
year={2020}
}

@article{distilbert,
  publtype={informal},
  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
  title={DistilBERT, a distilled version of {BERT}: smaller, faster, cheaper and lighter},
  year={2019},
  cdate={1546300800000},
  journal={CoRR},
  volume={abs/1910.01108}
}

@article{xie2020unsupervised,
  title={Unsupervised Data Augmentation for Consistency Training},
  author={Xie, Qizhe and Dai, Zihang and Hovy, Eduard and Luong, Thang and Le, Quoc},
  journal={NeurIPS},
  volume={33},
  year={2020}
}


@inproceedings{predictive_uncertainty,
  author    = {Andrey Malinin and
               Mark J. F. Gales},
  title     = {Predictive Uncertainty Estimation via Prior Networks},
  booktitle = {NeurIPS},
  year      = {2018},
  timestamp = {Mon, 16 May 2022 15:41:51 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/MalininG18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}



@inproceedings{scholar_vae,
    title = "Neural Models for Documents with Metadata",
    author = "Card, Dallas  and
      Tan, Chenhao  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 56th Annual Meeting of the ACL",
    month = jul,
    year = "2018",
    doi = "10.18653/v1/P18-1189",
    pages = "2031--2040",
    abstract = "Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration.",
}

@inproceedings{getting_clue,
title={Getting a {CLUE}: A  Method for Explaining Uncertainty Estimates},
author={Javier Antoran and Umang Bhatt and Tameem Adel and Adrian Weller and Jos{\'e} Miguel Hern{\'a}ndez-Lobato},
booktitle={ICLR},
year={2021}
}

@article{depeweg2017sensitivity,
  title={Sensitivity analysis for predictive uncertainty in Bayesian neural networks},
  author={Depeweg, Stefan and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Udluft, Steffen and Runkler, Thomas},
  journal={arXiv preprint arXiv:1712.03605},
  year={2017}
}

@article{ptm_survey,
	doi = {10.1007/s11431-020-1647-3},
	year = 2020,
	month = {sep},
	publisher = {Springer Science and Business Media {LLC}
},
	volume = {63},
	number = {10},
	pages = {1872--1897},
	author = {XiPeng Qiu and TianXiang Sun and YiGe Xu and YunFan Shao and Ning Dai and XuanJing Huang},
	title = {Pre-trained models for natural language processing: A survey},
	journal = {Science China Technological Sciences}
}

@misc{yarin-blackbox,
  doi = {10.48550/ARXIV.2107.00096},

  author = {Notin, Pascal and Hernández-Lobato, José Miguel and Gal, Yarin},
  
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Improving black-box optimization in VAE latent space using decoder uncertainty},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {Creative Commons Attribution 4.0 International}
}

@inproceedings{bert-geometry,
 author = {Reif, Emily and Yuan, Ann and Wattenberg, Martin and Viegas, Fernanda B and Coenen, Andy and Pearce, Adam and Kim, Been},
 booktitle = {NeurIPS},
 pages = {},
 title = {Visualizing and Measuring the Geometry of BERT},
 volume = {32},
 year = {2019}
}

@inproceedings{sngp,
 author = {Liu, Jeremiah and Lin, Zi and Padhy, Shreyas and Tran, Dustin and Bedrax Weiss, Tania and Lakshminarayanan, Balaji},
 booktitle = {NeurIPS},
 pages = {7498--7512},
 title = {Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness},
 volume = {33},
 year = {2020}
}






@inproceedings{dataset_emotion,
    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
    author = "Saravia, Elvis  and
      Liu, Hsien-Chi Toby  and
      Huang, Yen-Hao  and
      Wu, Junlin  and
      Chen, Yi-Shin",
    booktitle = "Proceedings of the 2018 Conference on EMNLP",
    month = oct # "-" # nov,
    year = "2018",
    doi = "10.18653/v1/D18-1404",
    pages = "3687--3697",
    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}

@inproceedings{dataset_goemotions,
    title = "{G}o{E}motions: A Dataset of Fine-Grained Emotions",
    author = "Demszky, Dorottya  and
      Movshovitz-Attias, Dana  and
      Ko, Jeongwoo  and
      Cowen, Alan  and
      Nemade, Gaurav  and
      Ravi, Sujith",
    booktitle = "Proceedings of the 58th Annual Meeting of the ACL",
    month = jul,
    year = "2020",
    doi = "10.18653/v1/2020.acl-main.372",
    pages = "4040--4054",
    abstract = "Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.",
}

@inproceedings{dataset_multinli,
    title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
    author = "Williams, Adina  and
      Nangia, Nikita  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 Conference of NAACL",
    month = jun,
    year = "2018",
    doi = "10.18653/v1/N18-1101",
    pages = "1112--1122",
    abstract = "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement.",
}



@InProceedings{monte_carlo_dropout,
  title = 	 {Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning},
  author = 	 {Gal, Yarin and Ghahramani, Zoubin},
  booktitle = 	 {Proceedings of The 33rd ICML},
  pages = 	 {1050--1059},
  year = 	 {2016},
  publisher =    {PMLR},
  abstract = 	 {Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs – extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout’s uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout’s uncertainty in deep reinforcement learning.}
}


@inproceedings{acl2022_uncertainty_transformers,
    title = "Uncertainty Estimation of Transformer Predictions for Misclassification Detection",
    author = "Vazhentsev, Artem  and
      Kuzmin, Gleb  and
      Shelmanov, Artem  and
      Tsvigun, Akim  and
      Tsymbalov, Evgenii  and
      Fedyanin, Kirill  and
      Panov, Maxim  and
      Panchenko, Alexander  and
      Gusev, Gleb  and
      Burtsev, Mikhail  and
      Avetisian, Manvel  and
      Zhukov, Leonid",
    booktitle = "Proceedings of the 60th Annual Meeting of the ACL",
    month = may,
    year = "2022",
    pages = "8237--8252",
    abstract = "Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classification tasks. Little attention has been paid to UE in natural language processing. To fill this gap, we perform a vast empirical investigation of state-of-the-art UE methods for Transformer models on misclassification detection in named entity recognition and text classification tasks and propose two computationally efficient modifications, one of which approaches or even outperforms computationally intensive methods.",
}



@InProceedings{bnn_wight_uncertainty,
  title = 	 {Weight Uncertainty in Neural Network},
  author = 	 {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
  booktitle = 	 {Proceedings of the 32nd ICML},
  pages = 	 {1613--1622},
  year = 	 {2015},
  publisher =    {PMLR},
  abstract = 	 {We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning.}
}

@inproceedings{how_certain_transformer,
    title = "How Certain is Your {T}ransformer?",
    author = "Shelmanov, Artem  and
      Tsymbalov, Evgenii  and
      Puzyrev, Dmitri  and
      Fedyanin, Kirill  and
      Panchenko, Alexander  and
      Panov, Maxim",
    booktitle = "Proceedings of the 16th Conference of the EACL",
    month = apr,
    year = "2021",
    doi = "10.18653/v1/2021.eacl-main.157",
    pages = "1833--1840",
    abstract = "In this work, we consider the problem of uncertainty estimation for Transformer-based models. We investigate the applicability of uncertainty estimates based on dropout usage at the inference stage (Monte Carlo dropout). The series of experiments on natural language understanding tasks shows that the resulting uncertainty estimates improve the quality of detection of error-prone instances. Special attention is paid to the construction of computationally inexpensive estimates via Monte Carlo dropout and Determinantal Point Processes.",
}

@inproceedings{deep_ensemble,
 author = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
 booktitle = {NeurIPS},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles},
 volume = {30},
 year = {2017}
}

@article{token_level_uncertainty,
  author    = {Yilin Shen and
               Wenhu Chen and
               Hongxia Jin},
  title     = {Modeling Token-level Uncertainty to Learn Unknown Concepts in {SLU}
               via Calibrated Dirichlet Prior {RNN}},
  journal   = {CoRR},
  volume    = {abs/2010.08101},
  year      = {2020},
  eprinttype = {arXiv},
  eprint    = {2010.08101},
  timestamp = {Wed, 21 Oct 2020 12:11:48 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2010-08101.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{token_level_classification_interpret,
    title = "Interpretation of {NLP} models through input marginalization",
    author = "Kim, Siwon  and
      Yi, Jihun  and
      Kim, Eunji  and
      Yoon, Sungroh",
    booktitle = "Proceedings of the 2020 Conference on EMNLP",
    month = nov,
    year = "2020",
    doi = "10.18653/v1/2020.emnlp-main.255",
    pages = "3154--3167",
    abstract = "To demystify the {``}black box{''} property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input. Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence lies out of the training data distribution, yielding misleading interpretations. In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy; we propose to marginalize each token out. We interpret various NLP models trained for sentiment analysis and natural language inference using the proposed method.",
}

@inproceedings{human_generate_token_adv,
    title = "Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification",
    author = "Mozes, Maximilian  and
      Bartolo, Max  and
      Stenetorp, Pontus  and
      Kleinberg, Bennett  and
      Griffin, Lewis",
    booktitle = "Proceedings of the 2021 Conference on EMNLP",
    month = nov,
    year = "2021",
    doi = "10.18653/v1/2021.emnlp-main.651",
    pages = "8258--8270",
    abstract = "Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TextFooler, Genetic, BAE and SememePSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient.",
}


@InProceedings{softmax_uncertain,
  title = 	 {On Calibration of Modern Neural Networks},
  author =       {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger},
  booktitle = 	 {Proceedings of the 34th ICML},
  pages = 	 {1321--1330},
  year = 	 {2017},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/guo17a/guo17a.pdf},
  abstract = 	 {Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling – a single-parameter variant of Platt Scaling – is surprisingly effective at calibrating predictions.}
}

@inproceedings{he-etal-2020-towards,
    title = "Towards More Accurate Uncertainty Estimation In Text Classification",
    author = "He, Jianfeng  and
      Zhang, Xuchao  and
      Lei, Shuo  and
      Chen, Zhiqian  and
      Chen, Fanglan  and
      Alhamadani, Abdulaziz  and
      Xiao, Bei  and
      Lu, ChangTien",
    booktitle = "Proceedings of the 2020 Conference on EMNLP",
    month = nov,
    year = "2020",
    doi = "10.18653/v1/2020.emnlp-main.671",
    pages = "8362--8372",
    abstract = "The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy. For instance, data-driven algorithms diagnosing diseases need accurate uncertainty score to decide whether additional but limited quantity of experts are needed for rectification. However, few uncertainty models focus on improving the performance of text classification where human resources are involved. To achieve this, we aim at generating accurate uncertainty score by improving the confidence of winning scores. Thus, a model called MSD, which includes three independent components as {``}mix-up{''}, {``}self-ensembling{''}, {``}distinctiveness score{''}, is proposed to improve the accuracy of uncertainty score by reducing the effect of overconfidence of winning score and considering the impact of different categories of uncertainty simultaneously. MSD can be applied with different Deep Neural Networks. Extensive experiments with ablation setting are conducted on four real-world datasets, on which, competitive results are obtained.",
}



@article{gal2016uncertainty,
  title={Uncertainty in deep learning},
  author={Gal, Yarin and others}
}


@inproceedings{transformer,
 author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
 booktitle = {NeurIPS},
 publisher = {Curran Associates, Inc.},
 title = {Attention is All you Need},
 volume = {30},
 year = {2017}
}

@inproceedings{quantify_uncertainty_nlp,
author = {Xiao, Yijun and Wang, William Yang},
title = {Quantifying Uncertainties in Natural Language Processing Tasks},
year = {2019},
isbn = {978-1-57735-809-1},
publisher = {AAAI Press},
doi = {10.1609/aaai.v33i01.33017322},
abstract = {Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.},
booktitle = {Proceedings of the 33rd AAAI},
}

@inproceedings{vae,
  author    = {Diederik P. Kingma and
               Max Welling},
  editor    = {Yoshua Bengio and
               Yann LeCun},
  title     = {Auto-Encoding Variational Bayes},
  booktitle = {2nd ICLR, {ICLR} 2014,
               Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings},
  year      = {2014},
  timestamp = {Thu, 04 Apr 2019 13:20:07 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/KingmaW13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{clark-etal-2019-bert,
    title = "What Does {BERT} Look at? {A}n {A}nalysis of {BERT}{'}s {A}ttention",
    author = "Clark, Kevin  and
      Khandelwal, Urvashi  and
      Levy, Omer  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    doi = "10.18653/v1/W19-4828",
    pages = "276--286",
    abstract = "Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT{'}s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT{'}s attention.",
}

@inproceedings{
Brunner2020On,
title={On Identifiability in Transformers},
author={Gino Brunner and Yang Liu and Damian Pascual and Oliver Richter and Massimiliano Ciaramita and Roger Wattenhofer},
booktitle={ICLR},
year={2020}
}

@inproceedings{zhou-srikumar-2022-closer,
    title = "A Closer Look at How Fine-tuning Changes {BERT}",
    author = "Zhou, Yichu  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 60th ACL",
    month = may,
    year = "2022",
    doi = "10.18653/v1/2022.acl-long.75",
    pages = "1046--1061",
    abstract = "Given the prevalence of pre-trained contextualized representations in today{'}s NLP, there have been many efforts to understand what information they contain, and why they seem to be universally successful. The most common approach to use these representations involves fine-tuning them for an end task. Yet, how fine-tuning changes the underlying embedding space is less studied. In this work, we study the English BERT family and use two probing techniques to analyze how fine-tuning changes the space. We hypothesize that fine-tuning affects classification performance by increasing the distances between examples associated with different labels. We confirm this hypothesis with carefully designed experiments on five different NLP tasks. Via these experiments, we also discover an exception to the prevailing wisdom that {``}fine-tuning always improves performance{''}. Finally, by comparing the representations before and after fine-tuning, we discover that fine-tuning does not introduce arbitrary changes to representations; instead, it adjusts the representations to downstream tasks while largely preserving the original spatial structure of the data points.",
}

@inproceedings{zhou-srikumar-2021-directprobe,
    title = "{D}irect{P}robe: Studying Representations without Classifiers",
    author = "Zhou, Yichu  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 2021 Conference of NAACL",
    month = jun,
    year = "2021",
    doi = "10.18653/v1/2021.naacl-main.401",
    pages = "5070--5083",
    abstract = "Understanding how linguistic structure is encoded in contextualized embedding could help explain their impressive performance across NLP. Existing approaches for probing them usually call for training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation{'}s goodness. In this work, we argue that doing so can be unreliable because different representations may need different classifiers. We develop a heuristic, DirectProbe, that directly studies the geometry of a representation by building upon the notion of a version space for a task. Experiments with several linguistic tasks and contextualized embeddings show that, even without training classifiers, DirectProbe can shine lights on how an embedding space represents labels and also anticipate the classifier performance for the representation.",
}

@inproceedings{marecek-rosa-2019-balustrades,
    title = "From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions",
    author = "Mare{\v{c}}ek, David  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    doi = "10.18653/v1/W19-4827",
    pages = "263--275",
    abstract = "We inspect the multi-head self-attention in Transformer NMT encoders for three source languages, looking for patterns that could have a syntactic interpretation. In many of the attention heads, we frequently find sequences of consecutive states attending to the same position, which resemble syntactic phrases. We propose a transparent deterministic method of quantifying the amount of syntactic information present in the self-attentions, based on automatically building and evaluating phrase-structure trees from the phrase-like sequences. We compare the resulting trees to existing constituency treebanks, both manually and by computing precision and recall.",
}

@inproceedings{NEURIPS2021_4e246a38,
 author = {Slack, Dylan and Hilgard, Anna and Singh, Sameer and Lakkaraju, Himabindu},
 booktitle = {NeurIPS},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {9391--9404},
 publisher = {Curran Associates, Inc.},
 title = {Reliable Post hoc Explanations: Modeling Uncertainty in Explainability},
 volume = {34},
 year = {2021}
}

@inproceedings{sun-marasovic-2021-effective,
    title = "Effective Attention Sheds Light On Interpretability",
    author = "Sun, Kaiser  and
      Marasovi{\'c}, Ana",
    booktitle = "Findings of the ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    doi = "10.18653/v1/2021.findings-acl.361",
    pages = "4126--4135",
}




@InProceedings{latent_gussian_process,
  title = 	 {{L}atent {G}aussian {P}rocesses for {D}istribution {E}stimation of {M}ultivariate {C}ategorical {D}ata},
  author = 	 {Gal, Yarin and Chen, Yutian and Ghahramani, Zoubin},
  booktitle = 	 {Proceedings of the 32nd ICML},
  pages = 	 {645--654},
  year = 	 {2015},
  publisher =    {PMLR},
  abstract = 	 {Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a non-linear transformation of a continuous latent space. Non-linearity captures multi-modality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.}
}

@inproceedings{desai-durrett-2020-calibration,
    title = "Calibration of Pre-trained Transformers",
    author = "Desai, Shrey  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2020 Conference on EMNLP",
    month = nov,
    year = "2020",
    doi = "10.18653/v1/2020.emnlp-main.21",
    pages = "295--302",
    abstract = "Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated. Specifically, do these models{'} posterior probabilities provide an accurate empirical measure of how likely the model is to be correct on a given example? We focus on BERT and RoBERTa in this work, and analyze their calibration across three tasks: natural language inference, paraphrase detection, and commonsense reasoning. For each task, we consider in-domain as well as challenging out-of-domain settings, where models face more examples they should be uncertain about. We show that: (1) when used out-of-the-box, pre-trained models are calibrated in-domain, and compared to baselines, their calibration error out-of-domain can be as much as 3.5x lower; (2) temperature scaling is effective at further reducing calibration error in-domain, and using label smoothing to deliberately increase empirical uncertainty helps calibrate posteriors out-of-domain.",
}

@article{Gupta_Kvernadze_Srikumar_2021, title={BERT \& Family Eat Word Salad: Experiments with Text Understanding}, volume={35}, DOI={10.1609/aaai.v35i14.17531}, abstractNote={In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. We define simple heuristics to construct such examples. Our experiments show that state-of-the-art models consistently fail to recognize them as ill-formed, and instead produce high confidence predictions on them. As a consequence of this phenomenon, models trained on sentences with randomly permuted word order perform close to state-of-the-art models. To alleviate these issues, we show that if models are explicitly trained to recognize invalid inputs, they can be robust to such attacks without a drop in performance.}, number={14}, journal={AAAI}, author={Gupta, Ashim and Kvernadze, Giorgi and Srikumar, Vivek}, year={2021}, month={May}, pages={12946-12954} }

@inproceedings{wallace-etal-2020-imitation,
    title = "Imitation Attacks and Defenses for Black-box Machine Translation Systems",
    author = "Wallace, Eric  and
      Stern, Mitchell  and
      Song, Dawn",
    booktitle = "Proceedings of the 2020 Conference on EMNLP",
    month = nov,
    year = "2020",
    doi = "10.18653/v1/2020.emnlp-main.446",
    pages = "5531--5546",
    abstract = "Adversaries may look to steal or attack black-box NLP systems, either for financial gain or to exploit model errors. One setting of particular interest is machine translation (MT), where models have high commercial value and errors can be costly. We investigate possible exploitations of black-box MT systems and explore a preliminary defense against such threats. We first show that MT systems can be stolen by querying them with monolingual sentences and training models to imitate their outputs. Using simulated experiments, we demonstrate that MT model stealing is possible even when imitation models have different input data or architectures than their target models. Applying these ideas, we train imitation models that reach within 0.6 BLEU of three production MT systems on both high-resource and low-resource language pairs. We then leverage the similarity of our imitation models to transfer adversarial examples to the production systems. We use gradient-based attacks that expose inputs which lead to semantically-incorrect translations, dropped content, and vulgar model outputs. To mitigate these vulnerabilities, we propose a defense that modifies translation outputs in order to misdirect the optimization of imitation models. This defense degrades the adversary{'}s BLEU score and attack success rate at some cost in the defender{'}s BLEU and inference speed.",
}

@InProceedings{pmlr-v139-xie21f,
  title = 	 {Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization},
  author =       {Xie, Sang Michael and Ma, Tengyu and Liang, Percy},
  booktitle = 	 {Proceedings of the 38th ICML},
  pages = 	 {11424--11435},
  year = 	 {2021},
  publisher =    {PMLR},
  abstract = 	 {We focus on prediction problems with structured outputs that are subject to output validity constraints, e.g. pseudocode-to-code translation where the code must compile. While labeled input-output pairs are expensive to obtain, "unlabeled" outputs, i.e. outputs without corresponding inputs, are freely available (e.g. code on GitHub) and provide information about output validity. Pre-training captures this structure by training a denoiser to denoise corrupted versions of unlabeled outputs. We first show that standard fine-tuning after pre-training destroys some of this structure. We then propose composed fine-tuning, which trains a predictor composed with the pre-trained denoiser. Importantly, the denoiser is fixed to preserve output structure. Like standard fine-tuning, the predictor is also initialized with the pre-trained denoiser. We prove for two-layer ReLU networks that composed fine-tuning significantly reduces the complexity of the predictor, thus improving generalization. Empirically, we show that composed fine-tuning improves over standard fine-tuning on two pseudocode-to-code translation datasets (3% and 6% relative). The improvement is magnified on out-of-distribution (OOD) examples (4% and 25% relative), suggesting that reducing predictor complexity improves OOD extrapolation.}
}

@inproceedings{nan-etal-2019-topic,
    title = "Topic Modeling with {W}asserstein Autoencoders",
    author = "Nan, Feng  and
      Ding, Ran  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 57th Annual Meeting of the ACL",
    month = jul,
    year = "2019",
    doi = "10.18653/v1/P19-1640",
    pages = "6345--6381",
    abstract = "We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.",
}

@inproceedings{
tolstikhin2018wasserstein,
title={Wasserstein Auto-Encoders},
author={Ilya Tolstikhin and Olivier Bousquet and Sylvain Gelly and Bernhard Schoelkopf},
booktitle={ICLR},
year={2018},
}

@misc{uncertainty_medical,
  doi = {10.48550/ARXIV.2003.10769},
  
  author = {Ghoshal, Biraja and Tucker, Allan},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{uncertainty_finance, title={Coupling Macro-Sector-Micro Financial Indicators for Learning Stock Representations with Less Uncertainty}, volume={35},  DOI={10.1609/aaai.v35i5.16568}, abstractNote={While the stock movement prediction has been intensively studied, existing work suffers from weak generalization because of the uncertainty in both data and modeling. On one hand, training a stock representation on stochastic stock data in an end-to-end manner may lead to excessive modeling, which involves model uncertainty. On the other, the analysis of correlating stock data with its relevant factors involves data uncertainty. To simultaneously address such uncertainty both from data and modeling perspectives, a fundamental yet challenging task is to learn a better stock representation with less uncertainty by considering hierarchical couplings from the macro-level to the sector-and micro-level. Accordingly, we propose a copula-based contrastive predictive coding (Co-CPC) method. Co-CPC first models the dependence between a certain stock sector and relevant macroeconomic variables that are sequential and heterogeneous, e.g., macro-variables are associated with different time intervals, scales, and distributions. Then, by involving a macro-sector context, stock representations are learned in a self-supervised way that can further be used for downstream tasks like stock movement prediction. Extensive experiments on two typical stock datasets verify the effectiveness of our Co-CPC method.}, number={5}, journal={AAAI}, author={Wang, Guifeng and Cao, Longbing and Zhao, Hongke and Liu, Qi and Chen, Enhong}, year={2021}, month={May}, pages={4418-4426} }

@book{sullivan2015introduction,
  title={Introduction to uncertainty quantification},
  author={Sullivan, Timothy John},
  volume={63},
  year={2015},
  publisher={Springer}
}

@article{Pei_Wang_Szarvas_2022, title={Transformer Uncertainty Estimation with Hierarchical Stochastic Attention}, volume={36}, DOI={10.1609/aaai.v36i10.21364}, abstractNote={Transformers are state-of-the-art in a wide range of NLP tasks and have also been applied to many real-world products. Understanding the reliability and certainty of transformer models is crucial for building trustable machine learning applications, e.g., medical diagnosis. Although many recent transformer extensions have been proposed, the study of the uncertainty estimation of transformer models is under-explored. In this work, we propose a novel way to enable transformers to have the capability of uncertainty estimation and, meanwhile, retain the original predictive performance. This is achieved by learning hierarchical stochastic self-attention that attends to values and a set of learnable centroids, respectively. Then new attention heads are formed with a mixture of sampled centroids using the Gumbel-Softmax trick. We theoretically show that the self-attention approximation by sampling from a Gumbel distribution is upper bounded. We empirically evaluate our model on two text classification tasks with both in-domain (ID) and out-of-domain (OOD) datasets. The experimental results demonstrate that our approach: (1) achieves the best predictive-uncertainty trade-off among compared methods; (2) exhibits very competitive (in most cases, better) predictive performance on ID datasets; (3) is on par with Monte Carlo dropout and ensemble methods in uncertainty estimation on OOD datasets.}, number={10}, journal={AAAI}, author={Pei, Jiahuan and Wang, Cheng and Szarvas, György}, year={2022}, month={Jun.}, pages={11147-11155} }

@ARTICLE{8371683,
  author={Kabir, H. M. Dipu and Khosravi, Abbas and Hosen, Mohammad Anwar and Nahavandi, Saeid},
  journal={IEEE Access}, 
  title={Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications}, 
  year={2018},
  volume={6},
  number={},
  pages={36218-36234},
  doi={10.1109/ACCESS.2018.2836917}}
  
  @inproceedings{10.5555/3045118.3045290,
author = {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
title = {Weight Uncertainty in Neural Networks},
year = {2015},
publisher = {JMLR.org},
abstract = {We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning.},
booktitle = {Proceedings of the 32nd ICML - Volume 37},
pages = {1613–1622},
numpages = {10},
location = {Lille, France},
series = {ICML'15}
}

@misc{mc_overcofident,
  doi = {10.48550/ARXIV.1906.11537},
  
  author = {Foong, Andrew Y. K. and Li, Yingzhen and Hernández-Lobato, José Miguel and Turner, Richard E.},
  
  keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {'In-Between' Uncertainty in Bayesian Neural Networks},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{Kenny_Keane_2021, title={On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17377}, DOI={10.1609/aaai.v35i13.17377}, abstractNote={There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet, counterfactual explanations have become very popular in eXplainable AI (XAI) due to their asserted computational, psychological, and legal benefits. In contrast however, semi-factuals (which appear to be equally useful) have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly because the non-discrete nature of images makes good counterfactuals difficult to define; indeed, generating plausible counterfactual images which lie on the data manifold is also problematic. This paper advances a novel method for generating plausible counterfactuals and semi-factuals for black-box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all “exceptional” features in a test image to be “normal” from the perspective of the counterfactual class, to generate plausible counterfactual images. Two controlled experiments compare this method to others in the literature, showing that PIECE generates highly plausible counterfactuals (and the best semi-factuals) on several benchmark measures.}, number={13}, journal={AAAI}, author={Kenny, Eoin M. and Keane, Mark T}, year={2021}, month={May}, pages={11575-11585} }

@misc{cola,
  doi = {10.48550/ARXIV.1805.12471},
  
  url = {https://arxiv.org/abs/1805.12471},
  
  author = {Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R.},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Neural Network Acceptability Judgments},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{
wang2018glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Alex Wang and Amanpreet Singh and Julian Michael and Felix Hill and Omer Levy and Samuel R. Bowman},
booktitle={ICLR},
year={2019}
}

@inproceedings{
hanqi,
title={Addressing Token Uniformity in Transformers via Singular Value Transformation},
author={Hanqi Yan and Lin Gui and Wenjie Li and Yulan He},
booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
year={2022},
url={https://openreview.net/forum?id=BtUxE_8i5l5}
}

@inproceedings{
kaushik2021explaining,
title={Explaining the Efficacy of Counterfactually Augmented Data},
author={Divyansh Kaushik and Amrith Setlur and Eduard H Hovy and Zachary Chase Lipton},
booktitle={ICLR},
year={2021},
url={https://openreview.net/forum?id=HHiiQKWsOcV}
}

@inproceedings{
Kaushik2020Learning,
title={Learning The Difference That Makes A Difference With Counterfactually-Augmented Data},
author={Divyansh Kaushik and Eduard Hovy and Zachary Lipton},
booktitle={ICLR},
year={2020},
url={https://openreview.net/forum?id=Sklgs0NFvr}
}


@misc{heiss2023nomu,
      title={NOMU: Neural Optimization-based Model Uncertainty}, 
      author={Jakob Heiss and Jakob Weissteiner and Hanna Wutte and Sven Seuken and Josef Teichmann},
      year={2023},
      eprint={2102.13640},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{yang-etal-2021-exploring,
    title = "Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis",
    author = "Yang, Linyi  and
      Li, Jiazheng  and
      Cunningham, Padraig  and
      Zhang, Yue  and
      Smyth, Barry  and
      Dong, Ruihai",
    booktitle = "ACL 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    doi = "10.18653/v1/2021.acl-long.26",
    pages = "306--316",
}

