Rishabh Agarwal

94 papers A* 33A 2B 1C 2Journal 50Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Process Reward Models That Think. Trans. Mach. Learn. Res. Muhammad Khalifa, Rishabh Agarwal, Lajanugen Logeswaran, Jaekyeom Kim, Hao Peng, Moontae Lee, Honglak Lee, Lu Wang
2025	A*	`conf`	Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. ICLR Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron C. Courville
2025	A*	`conf`	Generative Verifiers: Reward Modeling as Next-Token Prediction. ICLR Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal
2025	A*	`conf`	Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models. ICLR Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Aviral Kumar, Rishabh Agarwal, Sridhar Thiagarajan, Craig Boutilier, Aleksandra Faust
2025	J	`jnl`	Process Reward Models That Think. CoRR Muhammad Khalifa, Rishabh Agarwal, Lajanugen Logeswaran, Jaekyeom Kim, Hao Peng, Moontae Lee, Honglak Lee, Lu Wang
2025	J	`jnl`	Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers. CoRR Kusha Sareen, Morgane M. Moss, Alessandro Sordoni, Rishabh Agarwal, Arian Hosseini
2025	A*	`conf`	Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs. ICML Ziyu Ye, Rishabh Agarwal, Tianqi Liu, Rishabh Joshi, Sarmishta Velury, Quoc V. Le, Qijun Tan, Yuan Liu
2025	A*	`conf`	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. ICLR Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
2025	J	`jnl`	Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks. CoRR Abhranil Chandra, Ayush Agrawal, Arian Hosseini, Sebastian Fischmeister, Rishabh Agarwal, Navin Goyal, Aaron C. Courville
2025	A*	`conf`	Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling. ICLR Hritik Bansal, Arian Hosseini, Rishabh Agarwal, Vinh Q. Tran, Mehran Kazemi
2025	A*	`conf`	Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling. ICLR Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei Li, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister
2025	J	`jnl`	The Art of Scaling Reinforcement Learning Compute for LLMs. CoRR Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, Rachit Bansal, Sai Surya Duvvuri, Manzil Zaheer, Inderjit S. Dhillon, David Brandfonbrener, Rishabh Agarwal
2025	J	`jnl`	Towards Compute-Optimal Many-Shot In-Context Learning. CoRR Shahriar Golchin, Yanfei Chen, Rujun Han, Manan Gandhi, Tianli Yu, Swaroop Mishra, Mihai Surdeanu, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister
2025	A*	`conf`	Training Language Models to Self-Correct via Reinforcement Learning. ICLR Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust
2024	J	`jnl`	A comprehensive survey on answer generation methods using NLP. Nat. Lang. Process. J. Prashant Upadhyay, Rishabh Agarwal, Sumeet Dhiman, Abhinav Sarkar, Saumya Chaturvedi
2024	J	`jnl`	Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. CoRR Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron C. Courville
2024	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
2024	A*	`conf`	DistillSpec: Improving Speculative Decoding via Knowledge Distillation. ICLR Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal
2024	J	`jnl`	Don't Throw Away Data: Better Sequence Knowledge Distillation. CoRR Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn
2024	J	`jnl`	Evolving Alignment via Asymmetric Self-Play. CoRR Ziyu Ye, Rishabh Agarwal, Tianqi Liu, Rishabh Joshi, Sarmishta Velury, Quoc V. Le, Qijun Tan, Yuan Liu
2024	J	`jnl`	Generative Verifiers: Reward Modeling as Next-Token Prediction. CoRR Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal
2024	J	`jnl`	Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models. CoRR Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, Aleksandra Faust
2024	A*	`conf`	Many-Shot In-Context Learning. NeurIPS Rishabh Agarwal, Avi Singh, Lei Zhang, Bernd Bohnet, Luis Rosias, Stephanie C. Y. Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle
2024	J	`jnl`	Many-Shot In-Context Learning. CoRR Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle
2024	J	`jnl`	Not All LLM Reasoners Are Created Equal. CoRR Arian Hosseini, Alessandro Sordoni, Daniel Toyama, Aaron C. Courville, Rishabh Agarwal
2024	A*	`conf`	On scalable oversight with weak LLMs judging strong LLMs. NeurIPS Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah
2024	J	`jnl`	On scalable oversight with weak LLMs judging strong LLMs. CoRR Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah
2024	A*	`conf`	On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem
2024	J	`jnl`	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. CoRR Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
2024	J	`jnl`	SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning. CoRR Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
2024	A*	`conf`	SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning. ICML Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
2024	J	`jnl`	Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling. CoRR Hritik Bansal, Arian Hosseini, Rishabh Agarwal, Vinh Q. Tran, Mehran Kazemi
2024	J	`jnl`	Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling. CoRR Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei Li, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister
2024	A*	`conf`	Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. ICML Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal
2024	J	`jnl`	Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. CoRR Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal
2024	J	`jnl`	Training Language Models to Self-Correct via Reinforcement Learning. CoRR Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust
2024	J	`jnl`	Transformers Can Achieve Length Generalization But Not Robustly. CoRR Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou
2024	J	`jnl`	V-STaR: Training Verifiers for Self-Taught Reasoners. CoRR Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron C. Courville, Alessandro Sordoni, Rishabh Agarwal
2023	A	`conf`	A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. AISTATS Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare
2023	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
2023	A*	`conf`	Bigger, Better, Faster: Human-level Atari with human-level efficiency. ICML Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro
2023	J	`jnl`	Bigger, Better, Faster: Human-level Atari with human-level efficiency. CoRR Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro
2023	A*	`conf`	Bootstrapped Representations in Reinforcement Learning. ICML Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Bootstrapped Representations in Reinforcement Learning. CoRR Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	DistillSpec: Improving Speculative Decoding via Knowledge Distillation. CoRR Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal
2023	J	`jnl`	GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem
2023	A*	`conf`	Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. ICLR Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare
2023	J	`jnl`	Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore
2023	A*	`conf`	Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
2023	A*	`conf`	Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. ICLR Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare
2023	J	`jnl`	Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. CoRR Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare
2023	A*	`conf`	Revisiting Bellman Errors for Offline Model Selection. ICML Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok
2023	J	`jnl`	Revisiting Bellman Errors for Offline Model Selection. CoRR Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok
2023	—	`conf`	Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric. Tiny Papers @ ICLR Charline Le Lan, Rishabh Agarwal
2023	A*	`conf`	The Dormant Neuron Phenomenon in Deep Reinforcement Learning. ICML Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci
2023	J	`jnl`	The Dormant Neuron Phenomenon in Deep Reinforcement Learning. CoRR Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci
2023	A*	`conf`	Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. NeurIPS Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp
2023	J	`jnl`	Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. CoRR Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp
2022	J	`jnl`	A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. CoRR Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare
2022	J	`jnl`	Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
2022	A*	`conf`	Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. AAAI Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
2022	A*	`conf`	DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
2022	C	`conf`	Detection Of Crop Water Stress In Maize Using Drone Based Hyperspectral Imaging. IGARSS Jayantrao Mohite, Suryakant A. Sawant, Rishabh Agarwal, Ankur Pandit, Srinivasu Pappula
2022	J	`jnl`	Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
2022	A	`conf`	On the Generalization of Representations in Reinforcement Learning. AISTATS Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
2022	J	`jnl`	On the Generalization of Representations in Reinforcement Learning. CoRR Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
2022	A*	`conf`	Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
2021	A*	`conf`	Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
2021	J	`jnl`	Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
2021	J	`jnl`	Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. CoRR Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
2021	J	`jnl`	DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
2021	A*	`conf`	Deep Reinforcement Learning at the Edge of the Statistical Precipice. NeurIPS Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
2021	J	`jnl`	Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
2021	—	`conf`	Field Boundary Identification using Convolutional Neural Network and GIS on High Resolution Satellite Observations. Agro-Geoinformatics Suryakant A. Sawant, Rishabh Agarwal, Jayantrao Mohite, Ankur Pandit, Srinivasu Pappula
2021	A*	`conf`	Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. ICLR Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine
2021	A*	`conf`	Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton
2020	A*	`conf`	An Optimistic Perspective on Offline Reinforcement Learning. ICML Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
2020	—	`conf`	IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. SemEval@COLING Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi
2020	J	`jnl`	IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. CoRR Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi
2020	J	`jnl`	Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. CoRR Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine
2020	J	`jnl`	Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton
2020	A*	`conf`	RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas
2020	J	`jnl`	RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR 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
2020	A*	`conf`	Revisiting Fundamentals of Experience Replay. ICML William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
2020	J	`jnl`	Revisiting Fundamentals of Experience Replay. CoRR William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
2019	J	`jnl`	Evaluation Function Approximation for Scrabble. CoRR Rishabh Agarwal
2019	A*	`conf`	Learning to Generalize from Sparse and Underspecified Rewards. ICML Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi
2019	J	`jnl`	Learning to Generalize from Sparse and Underspecified Rewards. CoRR Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi
2019	—	`conf`	Measurement of shear forces during gripping tasks with a low-cost tactile sensing system. RoboSoft Rishabh Agarwal, Sarah Bergbreiter
2019	J	`jnl`	Striving for Simplicity in Off-policy Deep Reinforcement Learning. CoRR Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
2017	C	`conf`	Computing Theory Prime Implicates in Modal Logic. ISDA Manoj K. Raut, Tushar V. Kokane, Rishabh Agarwal
2017	—	`conf`	Development of a Low-Cost Education Platform: RoboMuse 4.0. AIR Ayush Shukla, Rishabjit Singh, Rishabh Agarwal, Muhammad Suhail, Subir K. Saha, Santanu Chaudhury
2017	B	`conf`	S-Pencil: A Smart Pencil Grip Monitoring System for Kids Using Sensors. GLOBECOM Prakhar Gupta, Rishabh Agarwal, Surbhi Saraswat, Hari Prabhat Gupta, Tanima Dutta
2016	—	`conf`	Touchless human-mobile robot interaction using a projectable interactive surface. SII Rishabh Agarwal, Prayag Sharma, Subir K. Saha, Takafumi Matsumaru