Publications

Journal Articles

• • Julian Zimmert and Yevgeny Seldin. Tsallis-INF: An optimal algorithm for stochastic and adversarial bandits. Journal of Machine Learning Research, 2021.

  • Stephan S. Lorenzen, Christian Igel, and Yevgeny Seldin. On PAC-Bayesian bounds for random forests. Machine Learning Journal, 2019.

  • Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, and Peter Auer. PAC-Bayesian inequalities for martingales. IEEE Transactions on Information Theory, 58(12), 2012.

  • Yevgeny Seldin and Naftali Tishby. PAC-Bayesian analysis of co-clustering and beyond. Journal of Machine Learning Research, 2010.

  • Gill Bejerano, Yevgeny Seldin, Hanah Margalit, and Naftali Tishby. Markovian domain fingerprinting: statistical segmentation of protein sequences. Bioinformatics, 17(10), 2001.

In Proceedings

• • Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicolò Cesa-Bianchi, and Yevgeny Seldin. Delayed Bandits: When Do Intermediate Observations Help? In Proceedings of the International Conference on Machine Learning (ICML), 2023.

  • Yi-Shan Wu and Yevgeny Seldin. Split-kl and PAC-Bayes-split-kl Inequalities for ternary random variables. In Advances in Neural Information Processing Systems (NeurIPS), 2022.

  • Chloé Rouyer, Dirk van der Hoeven, Nicolò Cesa-Bianchi, and Yevgeny Seldin. A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs. In Advances in Neural Information Processing Systems (NeurIPS), 2022.

  • Saeed Masoudian, Julian Zimmert, and Yevgeny Seldin. A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback. In Advances in Neural Information Processing Systems (NeurIPS), 2022.

  • Yi-Shan Wu, Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel, and Yevgeny Seldin. Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. In Advances in Neural Information Processing Systems (NeurIPS), 2021.

  • Saeed Masoudian and Yevgeny Seldin. Improved analysis of the Tsallis-INF algorithm in stochastically constrained adversarial bandits and stochastic bandits with adversarial corruptions. In Proceedings of the Conference on Learning Theory (COLT), 2021.

  • Chloé Rouyer, Yevgeny Seldin, and Nicolò Cesa-Bianchi. An algorithm for stochastic and adversarial bandits with switching costs. In Proceedings of the International Conference on Machine Learning (ICML), 2021.

  • Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel, and Yevgeny Seldin. Second order PAC-Bayesian bounds for the weighted majority vote. In Advances in Neural Information Processing Systems (NeurIPS), 2020. (spotlight)

  • Chloé Rouyer and Yevgeny Seldin. Tsallis-INF for decoupled exploration and exploitation in multi-armed bandits. In Proceedings of the Conference on Learning Theory (COLT), 2020.

  • Julian Zimmert and Yevgeny Seldin. An optimal algorithm for adversarial bandits with arbitrary delays. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

  • Tobias Sommer Thune, Nicolò Cesa-Bianchi, and Yevgeny Seldin. Nonstochastic multiarmed bandits with unrestricted delays. In Advances in Neural Information Processing Systems (NeurIPS), 2019.

  • Julian Zimmert and Yevgeny Seldin. An optimal algorithm for stochastic and adversarial bandits. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

  • Tobias Sommer Thune and Yevgeny Seldin. Adaptation to easy data in prediction with limited advice. In Advances in Neural Information Processing Systems (NeurIPS), 2018.

  • Julian Zimmert and Yevgeny Seldin. Factored Bandits. In Advances in Neural Information Processing Systems (NeurIPS), 2018.

  • Niklas Thiemann, Christian Igel, Olivier Wintenberger, and Yevgeny Seldin. A strongly quasiconvex PAC-Bayesian bound. In Proceedings of Machine Learning Research, 76 (ALT), 2017.

  • Yevgeny Seldin and Gábor Lugosi. An improved parametrization and analysis of the EXP3++ algorithm for stochastic and adversarial bandits. In Proceedings of Machine Learning Research, 65 (COLT), 2017.

  • Yevgeny Seldin and Gábor Lugosi. A lower bound for multi-armed bandits with expert advice. In proceedings of the 13th European Workshop on Reinforcement Learning (EWRL), 2016. Accepted.

  • Brian Brost, Yevgeny Seldin, Ingemar Johansson Cox, and Christina Lioma. Multi-dueling bandits and their application to online ranker evaluation. In proceeding of the 25th ACM International Conference on Information and Knowledge Management (CIKM), 2016. [Long version]

  • Brian Brost, Ingemar Johansson Cox, Yevgeny Seldin, and Christina Lioma. An improved multileaving algorithm for online ranker evaluation. In proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval : SIGIR '16. Association for Computing Machinery, 2016.

  • Yevgeny Seldin and Aleksandrs Slivkins. One practical algorithm for both stochastic and adversarial bandits. In JMLR Workshop and Conference Proceedings, 32 (ICML), 2014.

  • Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, and Yasin Abbasi-Yadkori. Prediction with limited advice and multiarmed bandits with paid observations. In JMLR Workshop and Conference Proceedings, 32 (ICML), 2014.

  • Ilya Tolstikhin and Yevgeny Seldin. PAC-Bayes-Empirical-Bernstein Inequality. In Advances in Neural Information Processing Systems (NIPS), 2013. (spotlight)

  • Yasin Abbasi-Yadkori, Peter Bartlett, Varun Kanade, Yevgeny Seldin, and Csaba Szepesvári. Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. In Advances in Neural Information Processing Systems (NIPS), 2013.

  • Yevgeny Seldin, Koby Crammer, and Peter Bartlett. Open problem: Adversarial multiarmed bandits with limited advice. In JMLR Workshop and Conference Proceedings, 30 (COLT), 2013.

  • Yevgeny Seldin, Csaba Szepesvári, Peter Auer, and Yasin Abbasi-Yadkori. Evaluation and analysis of the performance of the EXP3 algorithm in stochastic environments. In JMLR Workshop and Conference Proceedings, 24 (EWRL), 2013.

  • Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, and John Shawe-Taylor. PAC-Bayes-Bernstein inequality for martingales and its application to multiarmed bandits. In JMLR Workshop and Conference Proceedings, 26, 2012.

  • Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, and Ronald Ortner. PAC-Bayesian analysis of contextual bandits. In Advances in Neural Information Processing Systems (NIPS), 2011. [supplementary material]

  • Yevgeny Seldin and Naftali Tishby. PAC-Bayesian generalization bound for density estimation with application to co-clustering. In JMLR Workshop and Conference Proceedings, 5 (AISTATS), 2009.

  • Yevgeny Seldin and Naftali Tishby. Multi-classification by categorical features via clustering. In the proceedings of the 25th International Conference on Machine Learning (ICML), 2008.

  • Yevgeny Seldin, Noam Slonim, and Naftali Tishby. Information bottleneck for non co-occurrence data. In Advances in Neural Information Processing Systems (NIPS) 19, 2007.

  • Yevgeny Seldin, Gill Bejerano, and Naftali Tishby. Unsupervised sequence segmentation by a mixture of variable memory length Markov sources. In the proceedings of the 18th International Conference on Machine Learning (ICML), 2001.

Invited Book Chapters

• • Yevgeny Seldin and Bernhard Schölkopf. On the relations and differences between Popper dimension, exclusion dimension and VC-Dimension. Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik. Bernhard Schölkopf, Zhiyuan Luo, and Vladimir Vovk (Eds.), Springer, Heidelberg, 2013.

Other

• • Yevgeny Seldin. A PAC-Bayesian analysis of co-clustering, graph clustering, and pairwise clustering. ICML-2010 Workshop on Social Analytics: Learning from Human Interactions, 2010.

  • Yevgeny Seldin and Naftali Tishby. PAC-Bayesian bounds for discrete density estimation and co-clustering analysis. Foundations and New Trends of PAC-Bayesian Learning Workshop, 2010.

  • Yevgeny Seldin and Naftali Tishby. A PAC-Bayesian approach to formulation of clustering objectives. NIPS-2009 workshop “Clustering: Science or Art? Towards Principled Approaches”, 2009.

  • Yevgeny Seldin, Sonia Starik, and Michael Werman. Unsupervised clustering of images using their joint segmentation. In the 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV), 2003.

  • Yevgeny Seldin, Gill Bejerano, and Naftali Tishby. Unsupervised segmentation and classification of mixtures of Markovian sources. In the 33rd Symposium in the Interface of Computing Science and Statistics (Interface – Frontiers in Data Mining and Bioinformatics), 2001.