Model-Based Reinforcement Learning with Value-Targeted Regression. - Alex Ayoub; Zeyu Jia; Csaba Szepesvári; Mengdi Wang; Lin Yang 011 (ICML, 2020)

Randomized Exploration in Reinforcement Learning with General Value Function Approximation. - Haque Ishfaq; Qiwen Cui; Viet Nguyen; Alex Ayoub; Zhuoran Yang; Zhaoran Wang 001; Doina Precup; Lin Yang 011 (ICML, 2021)

An Elementary Proof that Q-learning Converges Almost Surely. - Matthew T. Regehr; Alex Ayoub (CoRR, 2021)

Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. - Zichen Vincent Zhang; Johannes Kirschner; Junxi Zhang; Francesco Zanini; Alex Ayoub; Masood Dehghan; Dale Schuurmans (NeurIPS, 2023)

Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning. - Erfan Miahi; Revan MacQueen; Alex Ayoub; Abbas Masoumzadeh; Martha White (Trans. Mach. Learn. Res., 2023)

Exploration via linearly perturbed loss minimisation. - David Janz; Shuai Liu; Alex Ayoub; Csaba Szepesvári (AISTATS, 2024)

Switching the Loss Reduces the Cost in Batch Reinforcement Learning. - Alex Ayoub; Kaiwen Wang; Vincent Liu; Samuel Robertson; James McInerney; Dawen Liang; Nathan Kallus; Csaba Szepesvári (ICML, 2024)

Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits. - Shuai Liu; Alex Ayoub; Flore Sentenac; Xiaoqi Tan; Csaba Szepesvári (NeurIPS, 2024)

Mitigating the Curse of Horizon in Monte-Carlo Returns. - Alex Ayoub; David Szepesvari; Francesco Zanini; Bryan Chan; Dhawal Gupta; Bruno Castro da Silva; Dale Schuurmans (RLJ, 2024)

Does Weighting Improve Matrix Factorization for Recommender Systems? - Alex Ayoub; Samuel Robertson; Dawen Liang; Harald Steck; Nathan Kallus (WWW, 2025)