Levine, Nir

9 publications

ICLR 2021 Balancing Constraints and Rewards with Meta-Gradient D4PG Dan A. Calian, Daniel J Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy Mann

MLJ 2021 Challenges of Real-World Reinforcement Learning: Definitions, Benchmarks and Analysis Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

ICMLW 2021 Task-Agnostic Continual Learning with Hybrid Probabilistic Models Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu

NeurIPS 2020 A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Gorur, Chris Harris, Dale Schuurmans

AAAI 2020 Improved Knowledge Distillation via Teacher Assistant Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, Hassan Ghasemzadeh

ICLR 2020 Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui

ICLR 2020 Robust Reinforcement Learning for Continuous Control with Model Misspecification Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

NeurIPS 2017 Rotting Bandits Nir Levine, Koby Crammer, Shie Mannor

NeurIPS 2017 Shallow Updates for Deep Reinforcement Learning Nir Levine, Tom Zahavy, Daniel J Mankowitz, Aviv Tamar, Shie Mannor