Alexander Ryabchenko

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I am a first-year PhD student at the University of Toronto, grateful to be advised by Murat Erdogdu, Wenlong Mou, and Stanislav Volgushev. My research interests are broadly in the mathematical foundations of machine learning, especially learning and decision-making with limited feedback, memory, or compute.

Before starting my PhD, I studied mathematics and computer science at the University of Toronto and did research at the Vector Institute, where I had the pleasure of working with Daniel Roy and Idan Attias.

Publications

  1. Adaptively Incorporating Directional Hints into Zeroth-Order Optimization

    Alexander Ryabchenko, Jian Qian, Wenlong Mou

    To appear, Advances in Neural Information Processing Systems (NeurIPS), .

  2. Capacity-Constrained Online Convex Optimization with Delayed Feedback

    Alexander Ryabchenko, Idan Attias, Daniel M. Roy

    To appear, Advances in Neural Information Processing Systems (NeurIPS), .

  3. A Reduction from Delayed to Immediate Feedback for Online Convex Optimization

    Alexander Ryabchenko, Idan Attias, Daniel M. Roy

    To appear, Advances in Neural Information Processing Systems (NeurIPS), .

  4. Reinforcement Learning with Action-Triggered Observations

    Alexander Ryabchenko, Wenlong Mou

    International Conference on Machine Learning (ICML), .

  5. Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs

    Alexander Ryabchenko, Idan Attias, Daniel M. Roy

    Conference on Learning Theory (COLT), .

Preprints

  1. Vanilla Policy Optimization Is Both Optimal and Differentially Private for Stochastic Contextual Bandits

    Idan Attias*, Orin Levy*, Alexander Ryabchenko*, Yishay Mansour, Uri Stemmer

  2. Optimal Learning in Games with Arbitrary Delays: Best-of-Both-Worlds for Self-Play and Adversarial Opponents

    Emmanuel Esposito*, Andrew Jacobsen*, Hao Qiu*, Alexander Ryabchenko*, Mengxiao Zhang*