| Unbiased learning-to-rank with biased feedback T Joachims, A Swaminathan, T Schnabel Proceedings of the Tenth ACM International Conference on Web Search and Data …, 2017 | 266 | 2017 |
| Recommendations as treatments: Debiasing learning and evaluation T Schnabel, A Swaminathan, A Singh, N Chandak, T Joachims international conference on machine learning, 1670-1679, 2016 | 249 | 2016 |
| Counterfactual risk minimization: Learning from logged bandit feedback A Swaminathan, T Joachims Proceedings of the 32nd International Conference on Machine Learning, 814-823, 2015 | 219 | 2015 |
| Batch learning from logged bandit feedback through counterfactual risk minimization A Swaminathan, T Joachims The Journal of Machine Learning Research 16 (1), 1731-1755, 2015 | 202 | 2015 |
| The self-normalized estimator for counterfactual learning A Swaminathan, T Joachims Advances in Neural Information Processing Systems, 3231-3239, 2015 | 195 | 2015 |
| Off-policy evaluation for slate recommendation A Swaminathan, A Krishnamurthy, A Agarwal, M Dudik, J Langford, ... Advances in Neural Information Processing Systems, 3632-3642, 2017 | 111 | 2017 |
| Deep learning with logged bandit feedback T Joachims, A Swaminathan, M de Rijke International Conference on Learning Representations, 2018 | 73 | 2018 |
| Temporal corpus summarization using submodular word coverage R Sipos, A Swaminathan, P Shivaswamy, T Joachims Proceedings of the 21st ACM international conference on Information and …, 2012 | 71 | 2012 |
| Off-policy policy gradient with state distribution correction Y Liu, A Swaminathan, A Agarwal, E Brunskill arXiv preprint arXiv:1904.08473, 2019 | 68 | 2019 |
| Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement T Joachims, A Swaminathan Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016 | 40 | 2016 |
| Large-scale Validation of Counterfactual Learning Methods: A Test-Bed D Lefortier, A Swaminathan, X Gu, T Joachims, M de Rijke arXiv preprint arXiv:1612.00367, 2016 | 31 | 2016 |
| Provably Good Batch Reinforcement Learning Without Great Exploration Y Liu, A Swaminathan, A Agarwal, E Brunskill arXiv preprint arXiv:2007.08202, 2020 | 28 | 2020 |
| Counterfactual risk minimization A Swaminathan, T Joachims Proceedings of the 24th International Conference on World Wide Web, 939-941, 2015 | 23 | 2015 |
| Mining videos from the web for electronic textbooks R Agrawal, M Christoforaki, S Gollapudi, A Kannan, K Kenthapadi, ... International Conference on Formal Concept Analysis, 219-234, 2014 | 23 | 2014 |
| NAIL: A General Interactive Fiction Agent M Hausknecht, R Loynd, G Yang, A Swaminathan, JD Williams arXiv preprint arXiv:1902.04259, 2019 | 22 | 2019 |
| Unbiased comparative evaluation of ranking functions T Schnabel, A Swaminathan, PI Frazier, T Joachims Proceedings of the 2016 ACM International Conference on the Theory of …, 2016 | 21 | 2016 |
| Active Learning for ML Enhanced Database Systems L Ma, B Ding, S Das, A Swaminathan Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 18 | 2020 |
| Beyond myopic inference in big data pipelines K Raman, A Swaminathan, J Gehrke, T Joachims Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 16 | 2013 |
| Provably good batch off-policy reinforcement learning without great exploration Y Liu, A Swaminathan, A Agarwal, E Brunskill Advances in Neural Information Processing Systems 33, 1264-1274, 2020 | 12 | 2020 |
| Computational query modeling and action selection A Agarwal, M Dudik, A Krishnamurthy, J Langford, A Swaminathan US Patent App. 15/136,688, 2017 | 11 | 2017 |
Thorsten JoachimsProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
