@inproceedings{kane2017active,
  title={Active classification with comparison queries},
  author={Kane, Daniel M and Lovett, Shachar and Moran, Shay and Zhang, Jiapeng},
  booktitle={2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)},
  pages={355--366},
  year={2017},
  organization={IEEE}
}
@inproceedings{garnett2012bayesian,
author = {Garnett, Roman and Krishnamurthy, Yamuna and Xiong, Xuehan and Schneider, Jeff and Mann, Richard},
title = {Bayesian Optimal Active Search and Surveying},
year = {2012},
isbn = {9781450312851},
publisher = {Omnipress},
address = {Madison, WI, USA},
abstract = {We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as possible. In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class. Numerous real-world problems can be framed in these terms, and in either case typical model-based concerns such as generalization error are only of secondary importance.We approach these problems via Bayesian decision theory; after choosing natural utility functions, we derive the optimal policies. We provide three contributions. In addition to introducing the active surveying problem, we extend previous work on active search in two ways. First, we prove a novel theoretical result, that less-myopic approximations to the optimal policy can outperform more-myopic approximations by any arbitrary degree. We then derive bounds that for certain models allow us to reduce (in practice dramatically) the exponential search space required by a na\"{\i}ve implementation of the optimal policy, enabling further lookahead while still ensuring that optimal decisions are always made.},
booktitle = {Proceedings of the 29th International Coference on International Conference on Machine Learning},
pages = {843–850},
numpages = {8},
location = {Edinburgh, Scotland},
series = {ICML'12}
}

@article{vankadara2019insights,
  title={Insights into ordinal embedding algorithms: A systematic evaluation},
  author={Vankadara, Leena Chennuru and Haghiri, Siavash and Lohaus, Michael and Wahab, Faiz Ul and von Luxburg, Ulrike},
  journal={arXiv preprint arXiv:1912.01666},
  year={2019}
}

@article{vankadara2023insights,
  title={Insights into ordinal embedding algorithms: A systematic evaluation},
  author={Vankadara, Leena Chennuru and Lohaus, Michael and Haghiri, Siavash and Wahab, Faiz Ul and von Luxburg, Ulrike},
  journal={Journal of Machine Learning Research (JMLR)},
  volume={24},
  number={191},
  pages={1--83},
  year={2023}
}



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  year={2018},
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}

@inproceedings{karbasi2012comparison,
author = {Karbasi, Amin and Ioannidis, Stratis and Massouli\'{e}, Laurent},
title = {Comparison-Based Learning with Rank Nets},
year = {2012},
isbn = {9781450312851},
publisher = {Omnipress},
address = {Madison, WI, USA},
abstract = {We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new strategy based on rank nets, and show that for target distributions with a bounded doubling constant, it finds the target in a number of comparisons close to the entropy of the target distribution and, hence, of the optimum. We extend these results to the case of noisy oracles, and compare this strategy to prior art over multiple datasets.},
booktitle = {Proceedings of the 29th International Coference on International Conference on Machine Learning},
pages = {1235–1242},
numpages = {8},
location = {Edinburgh, Scotland},
series = {ICML'12}
}

@inproceedings{kazemi2018comparison,
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  year={2018},
  organization={PMLR}
}

@article{karbasi2015small,
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  year={2015},
  publisher={IEEE}
}



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@article{meurer2017sympy,
 title = {SymPy: symbolic computing in Python},
 author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
 year = 2017,
 month = jan,
 keywords = {Python, Computer algebra system, Symbolics},
 abstract = {
            SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
         },
 volume = 3,
 pages = {e103},
 journal = {PeerJ Computer Science},
 issn = {2376-5992},
 url = {https://doi.org/10.7717/peerj-cs.103},
 doi = {10.7717/peerj-cs.103}
}