Learning to Remember Rare Events. - Lukasz Kaiser; Ofir Nachum; Aurko Roy; Samy Bengio (ICLR (Poster), 2017)

Improving Policy Gradient by Exploring Under-appreciated Rewards. - Ofir Nachum; Mohammad Norouzi 002; Dale Schuurmans (ICLR (Poster), 2017)

Bridging the Gap Between Value and Policy Based Reinforcement Learning. - Ofir Nachum; Mohammad Norouzi 002; Kelvin Xu; Dale Schuurmans (NIPS, 2017)

MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. - Ariel Gordon; Elad Eban; Ofir Nachum; Bo Chen 019; Tien-Ju Yang; Edward Choi (CoRR, 2017)

Arboral satisfaction: Recognition and LP approximation. - Erik D. Demaine; Varun Ganesan; Vladislav Kontsevoi; Qipeng Liu 001; Quanquan C. Liu; Fermi Ma; Ofir Nachum; Aaron Sidford; Erik Waingarten; Daniel Ziegler 002 (Inf. Process. Lett., 2017)

MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. - Ariel Gordon; Elad Eban; Ofir Nachum; Bo Chen 019; Hao Wu; Tien-Ju Yang; Edward Choi (CVPR, 2018)

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. - Ofir Nachum; Mohammad Norouzi 002; Kelvin Xu; Dale Schuurmans (ICLR (Poster), 2018)

Path Consistency Learning in Tsallis Entropy Regularized MDPs. - Yinlam Chow; Ofir Nachum; Mohammad Ghavamzadeh (ICML, 2018)

Smoothed Action Value Functions for Learning Gaussian Policies. - Ofir Nachum; Mohammad Norouzi 002; George Tucker; Dale Schuurmans (ICML, 2018)

Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. - Deirdre Quillen; Eric Jang; Ofir Nachum; Chelsea Finn; Julian Ibarz; Sergey Levine (ICRA, 2018)

A Lyapunov-based Approach to Safe Reinforcement Learning. - Yinlam Chow; Ofir Nachum; Edgar A. Duéñez-Guzmán; Mohammad Ghavamzadeh (NeurIPS, 2018)

Data-Efficient Hierarchical Reinforcement Learning. - Ofir Nachum; Shixiang Gu; Honglak Lee; Sergey Levine (NeurIPS, 2018)

Robustness Guarantees for Density Clustering. - Heinrich Jiang; Jennifer Jang; Ofir Nachum (AISTATS, 2019)

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real. - Ofir Nachum; Michael Ahn; Hugo Ponte; Shixiang Shane Gu; Vikash Kumar (CoRL, 2019)

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. - Ofir Nachum; Shixiang Gu; Honglak Lee; Sergey Levine (ICLR (Poster), 2019)

The Laplacian in RL: Learning Representations with Efficient Approximations. - Yifan Wu; George Tucker; Ofir Nachum (ICLR (Poster), 2019)

DeepMDP: Learning Continuous Latent Space Models for Representation Learning. - Carles Gelada; Saurabh Kumar 004; Jacob Buckman; Ofir Nachum; Marc G. Bellemare (ICML, 2019)

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. - Ofir Nachum; Yinlam Chow; Bo Dai 001; Lihong Li 001 (NeurIPS, 2019)

Lyapunov-based Safe Policy Optimization for Continuous Control. - Yinlam Chow; Ofir Nachum; Aleksandra Faust; Mohammad Ghavamzadeh; Edgar A. Duéñez-Guzmán (CoRR, 2019)

Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? - Ofir Nachum; Haoran Tang; Xingyu Lu; Shixiang Gu; Honglak Lee; Sergey Levine (CoRR, 2019)

Group-based Fair Learning Leads to Counter-intuitive Predictions. - Ofir Nachum; Heinrich Jiang (CoRR, 2019)

Behavior Regularized Offline Reinforcement Learning. - Yifan Wu; George Tucker; Ofir Nachum (CoRR, 2019)

AlgaeDICE: Policy Gradient from Arbitrary Experience. - Ofir Nachum; Bo Dai 001; Ilya Kostrikov; Yinlam Chow; Lihong Li 001; Dale Schuurmans (CoRR, 2019)

Identifying and Correcting Label Bias in Machine Learning. - Heinrich Jiang; Ofir Nachum (AISTATS, 2020)

Safe Policy Learning for Continuous Control. - Yinlam Chow; Ofir Nachum; Aleksandra Faust; Edgar A. Duéñez-Guzmán; Mohammad Ghavamzadeh (CoRL, 2020)

Imitation Learning via Off-Policy Distribution Matching. - Ilya Kostrikov; Ofir Nachum; Jonathan Tompson (ICLR, 2020)

BRPO: Batch Residual Policy Optimization. - Sungryull Sohn; Yinlam Chow; Jayden Ooi; Ofir Nachum; Honglak Lee; Ed H. Chi; Craig Boutilier (IJCAI, 2020)

CoinDICE: Off-Policy Confidence Interval Estimation. - Bo Dai 001; Ofir Nachum; Yinlam Chow; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2020)

Off-Policy Evaluation via the Regularized Lagrangian. - Mengjiao Yang 001; Ofir Nachum; Bo Dai 001; Lihong Li 001; Dale Schuurmans (NeurIPS, 2020)

Reinforcement Learning via Fenchel-Rockafellar Duality. - Ofir Nachum; Bo Dai 001 (CoRR, 2020)

D4RL: Datasets for Deep Data-Driven Reinforcement Learning. - Justin Fu; Aviral Kumar; Ofir Nachum; George Tucker; Sergey Levine (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation. - Ilya Kostrikov; Ofir Nachum (CoRR, 2020)

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. - Anurag Ajay; Aviral Kumar; Pulkit Agrawal 001; Sergey Levine; Ofir Nachum (ICLR, 2021)

Benchmarks for Deep Off-Policy Evaluation. - Justin Fu; Mohammad Norouzi 002; Ofir Nachum; George Tucker; Ziyu Wang 001; Alexander Novikov 001; Mengjiao Yang 001; Michael R. Zhang; Yutian Chen 001; Aviral Kumar; Cosmin Paduraru; Sergey Levine; et al. (ICLR, 2021)

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. - Tatsuya Matsushima; Hiroki Furuta; Yutaka Matsuo; Ofir Nachum; Shixiang Gu (ICLR, 2021)

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. - Michael R. Zhang; Thomas Paine; Ofir Nachum; Cosmin Paduraru; George Tucker; Ziyu Wang 001; Mohammad Norouzi 002 (ICLR, 2021)

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. - Hiroki Furuta; Tatsuya Matsushima; Tadashi Kozuno; Yutaka Matsuo; Sergey Levine; Ofir Nachum; Shixiang Shane Gu (ICML, 2021)

Offline Reinforcement Learning with Fisher Divergence Critic Regularization. - Ilya Kostrikov; Rob Fergus; Jonathan Tompson; Ofir Nachum (ICML, 2021)

Representation Matters: Offline Pretraining for Sequential Decision Making. - Mengjiao Yang 001; Ofir Nachum (ICML, 2021)

Provable Representation Learning for Imitation with Contrastive Fourier Features. - Ofir Nachum; Mengjiao Yang 001 (NeurIPS, 2021)

Near Optimal Policy Optimization via REPS. - Aldo Pacchiano; Jonathan N. Lee; Peter L. Bartlett; Ofir Nachum (NeurIPS, 2021)

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. - Michael R. Zhang; Tom Le Paine; Ofir Nachum; Cosmin Paduraru; George Tucker; Ziyu Wang 001; Mohammad Norouzi 002 (CoRR, 2021)

Offline Policy Selection under Uncertainty. - Mengjiao Yang 001; Bo Dai 001; Ofir Nachum; George Tucker; Dale Schuurmans (AISTATS, 2022)

Policy Gradients Incorporating the Future. - David Venuto; Elaine Lau; Doina Precup; Ofir Nachum (ICLR, 2022)

TRAIL: Near-Optimal Imitation Learning with Suboptimal Data. - Mengjiao Yang 001; Sergey Levine; Ofir Nachum (ICLR, 2022)

Model Selection in Batch Policy Optimization. - Jonathan Lee 002; George Tucker; Ofir Nachum; Bo Dai 001 (ICML, 2022)

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)

PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations. - Kuang-Huei Lee; Ofir Nachum; Tingnan Zhang; Sergio Guadarrama; Jie Tan 001; Wenhao Yu 003 (IROS, 2022)

Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters. - Seyed Kamyar Seyed Ghasemipour; Shixiang Shane Gu; Ofir Nachum (NeurIPS, 2022)

Multi-Game Decision Transformers. - Kuang-Huei Lee; Ofir Nachum; Mengjiao Yang 001; Lisa Lee; Daniel Freeman; Sergio Guadarrama; Ian Fischer; Winnie Xu; Eric Jang; Henryk Michalewski; Igor Mordatch (NeurIPS, 2022)

Oracle Inequalities for Model Selection in Offline Reinforcement Learning. - Jonathan N. Lee; George Tucker; Ofir Nachum; Bo Dai 001; Emma Brunskill (NeurIPS, 2022)

Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions. - Bogdan Mazoure; Ilya Kostrikov; Ofir Nachum; Jonathan Tompson (NeurIPS, 2022)

Chain of Thought Imitation with Procedure Cloning. - Mengjiao Yang 001; Dale Schuurmans; Pieter Abbeel; Ofir Nachum (NeurIPS, 2022)

A Mixture-of-Expert Approach to RL-based Dialogue Management. - Yinlam Chow; Aza Tulepbergenov; Ofir Nachum; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2022)

Joint Representation Training in Sequential Tasks with Shared Structure. - Aldo Pacchiano; Ofir Nachum; Nilesh Tripuraneni; Peter L. Bartlett (CoRR, 2022)

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. - Yevgen Chebotar; Quan Vuong; Karol Hausman; Fei Xia 002; Yao Lu 006; Alex Irpan; Aviral Kumar; Tianhe Yu; Alexander Herzog; Karl Pertsch; Keerthana Gopalakrishnan; Julian Ibarz; et al. (CoRL, 2023)

Contrastive Value Learning: Implicit Models for Simple Offline RL. - Bogdan Mazoure; Benjamin Eysenbach; Ofir Nachum; Jonathan Tompson (CoRL, 2023)

Understanding HTML with Large Language Models. - Izzeddin Gur; Ofir Nachum; Yingjie Miao; Mustafa Safdari; Austin V. Huang; Aakanksha Chowdhery; Sharan Narang; Noah Fiedel; Aleksandra Faust (EMNLP (Findings), 2023)

A Mixture-of-Expert Approach to RL-based Dialogue Management. - Yinlam Chow; Aza Tulepbergenov; Ofir Nachum; Dhawal Gupta; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier (ICLR, 2023)

Dichotomy of Control: Separating What You Can Control from What You Cannot. - Sherry Yang 001; Dale Schuurmans; Pieter Abbeel; Ofir Nachum (ICLR, 2023)

Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. - David Venuto; Sherry Yang 001; Pieter Abbeel; Doina Precup; Igor Mordatch; Ofir Nachum (ICML, 2023)

Supervised Pretraining Can Learn In-Context Reinforcement Learning. - Jonathan Lee 002; Annie Xie; Aldo Pacchiano; Yash Chandak; Chelsea Finn; Ofir Nachum; Emma Brunskill (NeurIPS, 2023)

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. - David Brandfonbrener; Ofir Nachum; Joan Bruna (NeurIPS, 2023)

Learning Universal Policies via Text-Guided Video Generation. - Yilun Du; Sherry Yang 001; Bo Dai 001; Hanjun Dai; Ofir Nachum; Josh Tenenbaum 001; Dale Schuurmans; Pieter Abbeel (NeurIPS, 2023)

RT-1: Robotics Transformer for Real-World Control at Scale. - Anthony Brohan; Noah Brown; Justice Carbajal; Yevgen Chebotar; Joseph Dabis; Chelsea Finn; Keerthana Gopalakrishnan; Karol Hausman; Alexander Herzog; Jasmine Hsu; Julian Ibarz; Brian Ichter; et al. (Robotics: Science and Systems, 2023)

Foundation Models for Decision Making: Problems, Methods, and Opportunities. - Sherry Yang 001; Ofir Nachum; Yilun Du; Jason Wei; Pieter Abbeel; Dale Schuurmans (CoRR, 2023)

Multimodal Web Navigation with Instruction-Finetuned Foundation Models. - Hiroki Furuta; Ofir Nachum; Kuang-Huei Lee; Yutaka Matsuo; Shixiang Shane Gu; Izzeddin Gur (CoRR, 2023)

Barkour: Benchmarking Animal-level Agility with Quadruped Robots. - Ken Caluwaerts; Atil Iscen; J. Chase Kew; Wenhao Yu 003; Tingnan Zhang; Daniel Freeman; Kuang-Huei Lee; Lisa Lee; Stefano Saliceti; Vincent Zhuang; Nathan Batchelor; Steven Bohez; et al. (CoRR, 2023)

Multimodal Web Navigation with Instruction-Finetuned Foundation Models. - Hiroki Furuta; Kuang-Huei Lee; Ofir Nachum; Yutaka Matsuo; Aleksandra Faust; Shixiang Shane Gu; Izzeddin Gur (ICLR, 2024)

The Design of the Barkour Benchmark for Robot Agility. - Wenhao Yu 003; Ken Caluwaerts; Atil Iscen; J. Chase Kew; Tingnan Zhang; Daniel Freeman; Lisa Lee; Stefano Saliceti; Vincent Zhuang; Nathan Batchelor; Steven Bohez; Federico Casarini; et al. (IROS, 2024)