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ORCIDOfir Nachum
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2020 – today
- 2021
[i37]Mengjiao Yang, Ofir Nachum:
Representation Matters: Offline Pretraining for Sequential Decision Making. CoRR abs/2102.05815 (2021)- [i36]Ilya Kostrikov, Jonathan Tompson, Rob Fergus, Ofir Nachum:
Offline Reinforcement Learning with Fisher Divergence Critic Regularization. CoRR abs/2103.08050 (2021) - [i35]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. CoRR abs/2103.09756 (2021) - [i34]Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu:
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. CoRR abs/2103.12726 (2021) - [i33]Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine:
Benchmarks for Deep Off-Policy Evaluation. CoRR abs/2103.16596 (2021) - 2020
- [c21]Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. AISTATS 2020: 702-712 - [c20]Ilya Kostrikov, Ofir Nachum, Jonathan Tompson:
Imitation Learning via Off-Policy Distribution Matching. ICLR 2020 - [c19]Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed Chi, Craig Boutilier:
BRPO: Batch Residual Policy Optimization. IJCAI 2020: 2824-2830 - [c18]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. NeurIPS 2020 - [c17]Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. NeurIPS 2020 - [i32]Ofir Nachum, Bo Dai:
Reinforcement Learning via Fenchel-Rockafellar Duality. CoRR abs/2001.01866 (2020) - [i31]Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed Chi, Craig Boutilier:
BRPO: Batch Residual Policy Optimization. CoRR abs/2002.05522 (2020) - [i30]Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine:
D4RL: Datasets for Deep Data-Driven Reinforcement Learning. CoRR abs/2004.07219 (2020) - [i29]Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu:
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. CoRR abs/2006.03647 (2020) - [i28]Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas:
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR abs/2006.13888 (2020) - [i27]Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. CoRR abs/2007.03438 (2020) - [i26]Ilya Kostrikov, Ofir Nachum:
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation. CoRR abs/2007.13609 (2020) - [i25]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. CoRR abs/2010.11652 (2020) - [i24]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. CoRR abs/2010.13611 (2020) - [i23]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. CoRR abs/2012.06919 (2020)
2010 – 2019
- 2019
- [c16]Heinrich Jiang, Jennifer Jang, Ofir Nachum:
Robustness Guarantees for Density Clustering. AISTATS 2019: 3342-3351 - [c15]Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Shane Gu, Vikash Kumar:
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real. CoRL 2019: 110-121 - [c14]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. ICLR (Poster) 2019 - [c13]Yifan Wu, George Tucker, Ofir Nachum:
The Laplacian in RL: Learning Representations with Efficient Approximations. ICLR (Poster) 2019 - [c12]Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare:
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. ICML 2019: 2170-2179 - [c11]Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. NeurIPS 2019: 2315-2325 - [i22]Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. CoRR abs/1901.04966 (2019) - [i21]Yinlam Chow, Ofir Nachum, Aleksandra Faust, Mohammad Ghavamzadeh, Edgar A. Duéñez-Guzmán:
Lyapunov-based Safe Policy Optimization for Continuous Control. CoRR abs/1901.10031 (2019) - [i20]Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare:
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. CoRR abs/1906.02736 (2019) - [i19]Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. CoRR abs/1906.04733 (2019) - [i18]Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, Vikash Kumar:
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real. CoRR abs/1908.05224 (2019) - [i17]Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine:
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? CoRR abs/1909.10618 (2019) - [i16]Ofir Nachum, Heinrich Jiang:
Group-based Fair Learning Leads to Counter-intuitive Predictions. CoRR abs/1910.02097 (2019) - [i15]Yifan Wu, George Tucker, Ofir Nachum:
Behavior Regularized Offline Reinforcement Learning. CoRR abs/1911.11361 (2019) - [i14]Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, Dale Schuurmans:
AlgaeDICE: Policy Gradient from Arbitrary Experience. CoRR abs/1912.02074 (2019) - [i13]Ilya Kostrikov, Ofir Nachum, Jonathan Tompson:
Imitation Learning via Off-Policy Distribution Matching. CoRR abs/1912.05032 (2019) - 2018
- [c10]Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, Edward Choi:
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. CVPR 2018: 1586-1595 - [c9]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. ICLR (Poster) 2018 - [c8]Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. ICML 2018: 978-987 - [c7]Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans:
Smoothed Action Value Functions for Learning Gaussian Policies. ICML 2018: 3689-3697 - [c6]Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine:
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. ICRA 2018: 6284-6291 - [c5]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Data-Efficient Hierarchical Reinforcement Learning. NeurIPS 2018: 3307-3317 - [c4]Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. NeurIPS 2018: 8103-8112 - [i12]Ofir Nachum, Yinlam Chow, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. CoRR abs/1802.03501 (2018) - [i11]Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine:
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. CoRR abs/1802.10264 (2018) - [i10]Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans:
Smoothed Action Value Functions for Learning Gaussian Policies. CoRR abs/1803.02348 (2018) - [i9]Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. CoRR abs/1805.07708 (2018) - [i8]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Data-Efficient Hierarchical Reinforcement Learning. CoRR abs/1805.08296 (2018) - [i7]Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. CoRR abs/1810.01257 (2018) - [i6]Yifan Wu, George Tucker, Ofir Nachum:
The Laplacian in RL: Learning Representations with Efficient Approximations. CoRR abs/1810.04586 (2018) - 2017
- [j1]Erik D. Demaine, Varun Ganesan, Vladislav Kontsevoi, Qipeng Liu, Quanquan C. Liu, Fermi Ma, Ofir Nachum, Aaron Sidford, Erik Waingarten, Daniel Ziegler:
Arboral satisfaction: Recognition and LP approximation. Inf. Process. Lett. 127: 1-5 (2017) - [c3]Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio:
Learning to Remember Rare Events. ICLR (Poster) 2017 - [c2]Ofir Nachum, Mohammad Norouzi, Dale Schuurmans:
Improving Policy Gradient by Exploring Under-appreciated Rewards. ICLR (Poster) 2017 - [c1]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Bridging the Gap Between Value and Policy Based Reinforcement Learning. NIPS 2017: 2775-2785 - [i5]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Bridging the Gap Between Value and Policy Based Reinforcement Learning. CoRR abs/1702.08892 (2017) - [i4]Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio:
Learning to Remember Rare Events. CoRR abs/1703.03129 (2017) - [i3]Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. CoRR abs/1707.01891 (2017) - [i2]Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Tien-Ju Yang, Edward Choi:
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. CoRR abs/1711.06798 (2017) - 2016
- [i1]Ofir Nachum, Mohammad Norouzi, Dale Schuurmans:
Improving Policy Gradient by Exploring Under-appreciated Rewards. CoRR abs/1611.09321 (2016)
Coauthor Index
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