Addressing Function Approximation Error in Actor-Critic Methods. - Scott Fujimoto; Herke van Hoof; David Meger (ICML, 2018)

Sentiment Analysis: It's Complicated! - Kian Kenyon-Dean; Eisha Ahmed; Scott Fujimoto; Jeremy Georges-Filteau; Christopher Glasz; Barleen Kaur; Auguste Lalande; Shruti Bhanderi; Robert Belfer; Nirmal Kanagasabai; Roman Sarrazingendron; Rohit Verma; et al. (NAACL-HLT, 2018)

Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation. - Edward J. Smith; Scott Fujimoto; David Meger (NeurIPS, 2018)

3D Object Super-Resolution. - Edward J. Smith; Scott Fujimoto; David Meger (CoRR, 2018)

Off-Policy Deep Reinforcement Learning without Exploration. - Scott Fujimoto; David Meger; Doina Precup (ICML, 2019)

GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects. - Edward J. Smith; Scott Fujimoto; Adriana Romero; David Meger (ICML, 2019)

Benchmarking Batch Deep Reinforcement Learning Algorithms. - Scott Fujimoto; Edoardo Conti; Mohammad Ghavamzadeh; Joelle Pineau (CoRR, 2019)

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. - Scott Fujimoto; David Meger; Doina Precup (NeurIPS, 2020)

A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. - Scott Fujimoto; David Meger; Doina Precup (ICML, 2021)

A Minimalist Approach to Offline Reinforcement Learning. - Scott Fujimoto; Shixiang Shane Gu (NeurIPS, 2021)

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. - Scott Fujimoto; David Meger; Doina Precup; Ofir Nachum; Shixiang Shane Gu (ICML, 2022)

IL-flOw: Imitation Learning from Observation using Normalizing Flows. - Wei-Di Chang; Juan Camilo Gamboa Higuera; Scott Fujimoto; David Meger; Gregory Dudek (CoRR, 2022)

For SALE: State-Action Representation Learning for Deep Reinforcement Learning. - Scott Fujimoto; Wei-Di Chang; Edward J. Smith; Shixiang Gu; Doina Precup; David Meger (NeurIPS, 2023)

Imitation Learning from Observation through Optimal Transport. - Wei-Di Chang; Scott Fujimoto; David Meger; Gregory Dudek (RLJ, 2024)

Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank. - Wenhao Zhan; Scott Fujimoto; Zheqing Zhu; Jason D. Lee; Daniel R. Jiang; Yonathan Efroni (CoRR, 2024)

Fairness in Reinforcement Learning with Bisimulation Metrics. - Sahand Rezaei-Shoshtari; Hanna Yurchyk; Scott Fujimoto; Doina Precup; David Meger (CoRR, 2024)

Towards General-Purpose Model-Free Reinforcement Learning. - Scott Fujimoto; Pierluca D'Oro; Amy Zhang 001; Yuandong Tian; Michael Rabbat (ICLR, 2025)

Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank. - Wenhao Zhan; Scott Fujimoto; Zheqing Zhu; Jason D. Lee; Daniel Jiang; Yonathan Efroni (ICLR, 2025)

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity. - Samin Yeasar Arnob; Scott Fujimoto; Doina Precup (CoRR, 2025)