Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. - Volodymyr Kuleshov; Evgenii Nikishin; Shantanu Thakoor; Tingfung Lau; Stefano Ermon (CoRR, 2021)

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. - Evgenii Nikishin; Romina Abachi; Rishabh Agarwal; Pierre-Luc Bacon (AAAI, 2022)

The Primacy Bias in Deep Reinforcement Learning. - Evgenii Nikishin; Max Schwarzer; Pierluca D'Oro; Pierre-Luc Bacon; Aaron C. Courville (ICML, 2022)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. - Pierluca D'Oro; Max Schwarzer; Evgenii Nikishin; Pierre-Luc Bacon; Marc G. Bellemare; Aaron C. Courville (ICLR, 2023)

Understanding Plasticity in Neural Networks. - Clare Lyle; Zeyu Zheng; Evgenii Nikishin; Bernardo Ávila Pires; Razvan Pascanu; Will Dabney (ICML, 2023)

Deep Reinforcement Learning with Plasticity Injection. - Evgenii Nikishin; Junhyuk Oh; Georg Ostrovski; Clare Lyle; Razvan Pascanu; Will Dabney; André Barreto 001 (NeurIPS, 2023)

The Curse of Diversity in Ensemble-Based Exploration. - Zhixuan Lin; Pierluca D'Oro; Evgenii Nikishin; Aaron C. Courville (ICLR, 2024)

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. - Simon Dufort-Labbé; Pierluca D'Oro; Evgenii Nikishin; Razvan Pascanu; Pierre-Luc Bacon; Aristide Baratin (CoRR, 2024)

Forgetting Transformer: Softmax Attention with a Forget Gate. - Zhixuan Lin; Evgenii Nikishin; Xu Owen He; Aaron C. Courville (ICLR, 2025)

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. - Simon Dufort-Labbé; Pierluca D'Oro; Evgenii Nikishin; Irina Rish; Pierre-Luc Bacon; Razvan Pascanu; Aristide Baratin (Trans. Mach. Learn. Res., 2025)