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2020 – today
- 2024
- [c53]Hiroki Furuta, Kuang-Huei Lee, Ofir Nachum, Yutaka Matsuo, Aleksandra Faust, Shixiang Shane Gu, Izzeddin Gur:
Multimodal Web Navigation with Instruction-Finetuned Foundation Models. ICLR 2024 - 2023
- [c52]Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson:
Contrastive Value Learning: Implicit Models for Simple Offline RL. CoRL 2023: 1257-1267 - [c51]Yevgen Chebotar, Quan Vuong, Karol Hausman, Fei Xia, Yao Lu, Alex Irpan, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Anand Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine:
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. CoRL 2023: 3909-3928 - [c50]Izzeddin Gur, Ofir Nachum, Yingjie Miao, Mustafa Safdari, Austin V. Huang, Aakanksha Chowdhery, Sharan Narang, Noah Fiedel, Aleksandra Faust:
Understanding HTML with Large Language Models. EMNLP (Findings) 2023: 2803-2821 - [c49]Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Dhawal Gupta, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier:
A Mixture-of-Expert Approach to RL-based Dialogue Management. ICLR 2023 - [c48]Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. ICLR 2023 - [c47]David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. ICML 2023: 35024-35036 - [c46]Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS 2023 - [c45]David Brandfonbrener, Ofir Nachum, Joan Bruna:
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. NeurIPS 2023 - [c44]Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. NeurIPS 2023 - [c43]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag R. Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-1: Robotics Transformer for Real-World Control at Scale. Robotics: Science and Systems 2023 - [i63]Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. CoRR abs/2302.00111 (2023) - [i62]Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel, Dale Schuurmans:
Foundation Models for Decision Making: Problems, Methods, and Opportunities. CoRR abs/2303.04129 (2023) - [i61]Hiroki Furuta, Ofir Nachum, Kuang-Huei Lee, Yutaka Matsuo, Shixiang Shane Gu, Izzeddin Gur:
Multimodal Web Navigation with Instruction-Finetuned Foundation Models. CoRR abs/2305.11854 (2023) - [i60]Ken Caluwaerts, Atil Iscen, J. Chase Kew, Wenhao Yu, Tingnan Zhang, Daniel Freeman, Kuang-Huei Lee, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, José Enrique Chen, Omar Cortes, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Linda Luu, Ofir Nachum, Ken Oslund, Jason Powell, Diego Reyes, Francesco Romano, Fereshteh Sadeghi, Ron Sloat, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan:
Barkour: Benchmarking Animal-level Agility with Quadruped Robots. CoRR abs/2305.14654 (2023) - [i59]David Brandfonbrener, Ofir Nachum, Joan Bruna:
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. CoRR abs/2305.16985 (2023) - [i58]Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR abs/2306.14892 (2023) - [i57]Yevgen Chebotar, Quan Vuong
, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine:
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. CoRR abs/2309.10150 (2023) - 2022
- [c42]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. AISTATS 2022: 4376-4396 - [c41]David Venuto, Elaine Lau, Doina Precup, Ofir Nachum:
Policy Gradients Incorporating the Future. ICLR 2022 - [c40]Mengjiao Yang, Sergey Levine, Ofir Nachum:
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data. ICLR 2022 - [c39]Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu:
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. ICML 2022: 6918-6943 - [c38]Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. ICML 2022: 12542-12569 - [c37]Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, Wenhao Yu:
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations. IROS 2022: 1447-1454 - [c36]Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum:
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters. NeurIPS 2022 - [c35]Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, Igor Mordatch:
Multi-Game Decision Transformers. NeurIPS 2022 - [c34]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS 2022 - [c33]Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson:
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions. NeurIPS 2022 - [c32]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. NeurIPS 2022 - [i56]Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu:
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. CoRR abs/2201.12417 (2022) - [i55]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Chain of Thought Imitation with Procedure Cloning. CoRR abs/2205.10816 (2022) - [i54]Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum:
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters. CoRR abs/2205.13703 (2022) - [i53]Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch:
Multi-Game Decision Transformers. CoRR abs/2205.15241 (2022) - [i52]Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier:
A Mixture-of-Expert Approach to RL-based Dialogue Management. CoRR abs/2206.00059 (2022) - [i51]Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett:
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR abs/2206.12441 (2022) - [i50]Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, Wenhao Yu:
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations. CoRR abs/2207.13224 (2022) - [i49]Izzeddin Gur, Ofir Nachum, Yingjie Miao, Mustafa Safdari, Austin V. Huang, Aakanksha Chowdhery, Sharan Narang, Noah Fiedel, Aleksandra Faust:
Understanding HTML with Large Language Models. CoRR abs/2210.03945 (2022) - [i48]Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. CoRR abs/2210.13435 (2022) - [i47]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR abs/2211.02016 (2022) - [i46]Bogdan Mazoure, Benjamin Eysenbach
Contrastive Value Learning: Implicit Models for Simple Offline RL. CoRR abs/2211.02100 (2022) - [i45]David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. CoRR abs/2211.13337 (2022) - [i44]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke
RT-1: Robotics Transformer for Real-World Control at Scale. CoRR abs/2212.06817 (2022) - 2021
- [c31]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. ICLR 2021 - [c30]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. ICLR 2021 - [c29]Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu:
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. ICLR 2021 - [c28]Michael R. Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi:
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. ICLR 2021 - [c27]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. ICML 2021: 3541-3552 - [c26]Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum:
Offline Reinforcement Learning with Fisher Divergence Critic Regularization. ICML 2021: 5774-5783 - [c25]Mengjiao Yang, Ofir Nachum:
Representation Matters: Offline Pretraining for Sequential Decision Making. ICML 2021: 11784-11794 - [c24]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. NeurIPS 2021: 1100-1110 - [c23]Ofir Nachum, Mengjiao Yang:
Provable Representation Learning for Imitation with Contrastive Fourier Features. NeurIPS 2021: 30100-30112 - [i43]Mengjiao Yang, Ofir Nachum:
Representation Matters: Offline Pretraining for Sequential Decision Making. CoRR abs/2102.05815 (2021) - [i42]Ilya Kostrikov, Jonathan Tompson, Rob Fergus, Ofir Nachum:
Offline Reinforcement Learning with Fisher Divergence Critic Regularization. CoRR abs/2103.08050 (2021) - [i41]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. CoRR abs/2103.09756 (2021) - [i40]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) - [i39]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) - [i38]Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi:
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization. CoRR abs/2104.13877 (2021) - [i37]Ofir Nachum, Mengjiao Yang:
Provable Representation Learning for Imitation with Contrastive Fourier Features. CoRR abs/2105.12272 (2021) - [i36]David Venuto, Elaine Lau, Doina Precup, Ofir Nachum:
Policy Gradients Incorporating the Future. CoRR abs/2108.02096 (2021) - [i35]Mengjiao Yang, Sergey Levine, Ofir Nachum:
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data. CoRR abs/2110.14770 (2021) - [i34]Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson:
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions. CoRR abs/2111.14629 (2021) - [i33]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. CoRR abs/2112.12320 (2021) - 2020
- [c22]Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. AISTATS 2020: 702-712 - [c21]Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
Safe Policy Learning for Continuous Control. CoRL 2020: 801-821 - [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 H. 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 H. 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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last updated on 2024-11-13 23:47 CET by the dblp team
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