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  • 2021

    Adversarial Dueling Bandits

    Saha, A., Koren, T. & Mansour, Y., 2021, Proceedings of the 38th International Conference on Machine Learning. Meila, M. & Zhang, T. (eds.). PMLR, p. 9235-9244 10 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Algorithmic Instabilities of Accelerated Gradient Descent

    Attia, A. & Koren, T., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 1204-1214 11 p. (Advances in Neural Information Processing Systems; vol. 2).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Asynchronous Stochastic Optimization Robust to Arbitrary Delays

    Cohen, A., Daniely, A., Drori, Y., Koren, T. & Schain, M., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 9024-9035 12 p. (Advances in Neural Information Processing Systems; vol. 11).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs

    Erez, L. & Koren, T., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 28511-28521 11 p. (Advances in Neural Information Processing Systems; vol. 34).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Dueling Convex Optimization

    Saha, A., Koren, T. & Mansour, Y., 2021, Proceedings of the 38th International Conference on Machine Learning. Meila, M. & Zhang, T. (eds.). PMLR, p. 9245-9254 10 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Lazy OCO: Online Convex Optimization on a Switching Budget

    Sherman, U. & Koren, T., 2021, Proceedings of Thirty Fourth Conference on Learning Theory. Belkin, M. & Kpotufe, S. (eds.). PMLR, p. 3972-3988 17 p. (Proceedings of Machine Learning Research; vol. 134).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Never Go Full Batch (in Stochastic Convex Optimization)

    Amir, I., Koren, T., Carmon, Y. & Livni, R., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 25033-25043 11 p. (Advances in Neural Information Processing Systems; vol. 30).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Online Markov Decision Processes with Aggregate Bandit Feedback

    Cohen, A., Kaplan, H., Koren, T. & Mansour, Y., 2021, Proceedings of Thirty Fourth Conference on Learning Theory. Belkin, M. & Kpotufe, S. (eds.). PMLR, p. 1301-1329 29 p. (Proceedings of Machine Learning Research; vol. 134).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret.

    Cassel, A. B. & Koren, T., 2021, Proceedings of the 38th International Conference on Machine Learning. Meila, M. & Zhang, T. (eds.). PMLR, p. 1304-1313 10 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Optimal Rates for Random Order Online Optimization

    Sherman, U., Koren, T. & Mansour, Y., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 2097-2108 12 p. (Advances in Neural Information Processing Systems; vol. 3).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry.

    Asi, H., Feldman, V., Koren, T. & Talwar, K., 2021, Proceedings of the 38th International Conference on Machine Learning. Meila, M. & Zhang, T. (eds.). PMLR, p. 393-403 11 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • SGD Generalizes Better Than GD (And Regularization Doesn't Help)

    Amir, I., Koren, T. & Livni, R., 2021, Proceedings of Thirty Fourth Conference on Learning Theory. Belkin, M. & Kpotufe, S. (eds.). PMLR, p. 63-92 30 p. (Proceedings of Machine Learning Research; vol. 134).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions

    Lancewicki, T., Segal, S., Koren, T. & Mansour, Y., 2021, Proceedings of the 38th International Conference on Machine Learning. Meila, M. & Zhang, T. (eds.). PMLR, p. 5969-5978 10 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2020

    Logarithmic regret for learning linear quadratic regulators efficiently

    Cassel, A., Cohen, A. & Koren, T., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 1305-1314 10 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-2).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Open Problem: Tight Convergence of SGD in Constant Dimension

    Koren, T. & Segal, S., 2020, Proceedings of Thirty Third Conference on Learning Theory. Abernethy, J. & Agarwal, S. (eds.). PMLR, p. 3847-3851 5 p. (Proceedings of Machine Learning Research; vol. 125).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Private stochastic convex optimization: Optimal rates in linear time

    Feldman, V., Koren, T. & Talwar, K., 8 Jun 2020, STOC 2020 - Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing. Makarychev, K., Makarychev, Y., Tulsiani, M., Kamath, G. & Chuzhoy, J. (eds.). Association for Computing Machinery, p. 439-449 11 p. (Proceedings of the Annual ACM Symposium on Theory of Computing).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2019

    Better Algorithms for Stochastic Bandits with Adversarial Corruptions

    Gupta, A., Koren, T. & Talwar, K., 2019, Proceedings of the Thirty-Second Conference on Learning Theory. Beygelzimer, A. & Hsu, D. (eds.). PMLR, p. 1562-1578 17 p. (Proceedings of Machine Learning Research; vol. 99).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Learning linear-quadratic regulators efficiently with only √T regret

    Cohen, A., Koren, T. & Mansour, Y., 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 2313-2322 10 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Semi-cyclic stochastic gradient descent

    Eichner, H., Koren, T., McMahan, H. B., Srebro, N. & Talwar, K., 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 3165-3177 13 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2018

    Online Linear Quadratic Control

    Cohen, A., Hassidim, A., Koren, T., Lazic, N., Mansour, Y. & Talwar, K., 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 1667-1681 15 p. (35th International Conference on Machine Learning, ICML 2018; vol. 3).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Shampoo: Preconditioned stochastic tensor optimization

    Gupta, V., Koren, T. & Singer, Y., 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 2956-2964 9 p. (35th International Conference on Machine Learning, ICML 2018; vol. 4).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2017

    Bandits with Movement Costs and Adaptive Pricing

    Koren, T., Livni, R. & Mansour, Y., 2017, Proceedings of the 2017 Conference on Learning Theory. Kale, S. & Shamir, O. (eds.). PMLR, p. 1242-1268 27 p. (Proceedings of Machine Learning Research; vol. 65).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Tight Bounds for Bandit Combinatorial Optimization.

    Cohen, A., Hazan, T. & Koren, T., 2017, Proceedings of the 2017 Conference on Learning Theory. Kale, S. & Shamir, O. (eds.). PMLR, p. 629-642 14 p. (Proceedings of Machine Learning Research; vol. 65).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2016

    Online Learning with Feedback Graphs Without the Graphs.

    Cohen, A., Hazan, T. & Koren, T., 2016, International Conference on Machine Learning, 20-22 June 2016, New York, New York, USA. Balcan, M. F. & Weinberger, K. Q. (eds.). PMLR, p. 811-819 9 p. (Proceedings of Machine Learning Research; vol. 48).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Online learning with low rank experts

    Hazan, E., Koren, T., Livni, R. & Mansour, Y., 6 Jun 2016, 29th Annual Conference on Learning Theory. Feldman, V., Rakhlin, A. & Shamir, O. (eds.). PMLR, p. 1096-1114 19 p. (Proceedings of Machine Learning Research; vol. 49).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • The computational power of optimization in online learning

    Hazan, E. & Koren, T., 19 Jun 2016, STOC 2016 - Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing. Mansour, Y. & Wichs, D. (eds.). Association for Computing Machinery, p. 128-141 14 p. (Proceedings of the Annual ACM Symposium on Theory of Computing; vol. 19-21-June-2016).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
  • 2015

    Bandit convex optimization: √T regret in one dimension

    Bubeck, S., Dekel, O., Koren, T. & Peres, Y., 2015, Proceedings of The 28th Conference on Learning Theory. Grünwald, P., Hazan, E. & Kale, S. (eds.). 2015 ed. PMLR, Vol. 40. (Proceedings of Machine Learning Research; vol. 40).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Online learning with feedback graphs: Beyond bandits

    Alon, N., Cesa-Bianchi, N., Dekel, O. & Koren, T., 2015, Proceedings of The 28th Conference on Learning Theory. Grünwald, P., Hazan, E. & Kale, S. (eds.). PMLR, Vol. 40. p. 23-35 13 p. (Proceedings of Machine Learning Research; vol. 40).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2014

    Bandits with switching costs: T2/3 regret

    Dekel, O., Ding, J., Koren, T. & Peres, Y., 2014, STOC 2014 - Proceedings of the 2014 ACM Symposium on Theory of Computing. Association for Computing Machinery, p. 459-467 9 p. (Proceedings of the Annual ACM Symposium on Theory of Computing).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Chasing ghosts: Competing with stateful policies

    Feige, U., Koren, T. & Tennenholtz, M., 7 Dec 2014, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS. IEEE Computer Society, p. 100-109 10 p. 6978994. (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Logistic regression: Tight bounds for stochastic and online optimization

    Hazan, E., Koren, T. & Levy, K. Y., 2014, Proceedings of The 27th Conference on Learning Theory. Balcan, M. F., Feldman, V. & Szepesvári, C. (eds.). Vol. 35. p. 197-209 13 p. (Proceedings of Machine Learning Research; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Online learning with composite loss functions

    Dekel, O., Ding, J., Koren, T. & Peres, Y., 2014, Proceedings of The 27th Conference on Learning Theory. Balcan, M. F., Feldman, V. & Szepesvári, C. (eds.). PMLR, p. 1214-1231 18 p. (Proceedings of Machine Learning Research; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2013

    Open problem: Fast stochastic exp-concave optimization

    Koren, T., 2013, Proceedings of the 26th Annual Conference on Learning Theory. Shalev-Shwartz, S. & Steinwart, I. (eds.). PMLR, p. 1073-1075 3 p. (Proceedings of Machine Learning Research; vol. 30).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2012

    Linear regression with limited observation

    Hazan, E. & Koren, T., 2012, Proceedings of the 29th International Conference on Machine Learning, ICML 2012. p. 807-814 8 p. (Proceedings of the 29th International Conference on Machine Learning, ICML 2012; vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Supervised system identification based on local PCA models

    Koren, T., Talmon, R. & Cohen, I., 2012, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. p. 541-544 4 p. 6287936. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • 2011

    Beating SGD: Learning SVMs in sublinear time

    Hazan, E., Koren, T. & Srebro, N., 2011, Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011. (Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review