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ORCIDRishabh Agarwal
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2020 – today
- 2024
[j2]Prashant Upadhyay
, Rishabh Agarwal, Sumeet Dhiman, Abhinav Sarkar, Saumya Chaturvedi:
A comprehensive survey on answer generation methods using NLP. Nat. Lang. Process. J. 8: 100088 (2024)- [j1]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:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c34]Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem:
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR 2024 - [c33]Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal:
DistillSpec: Improving Speculative Decoding via Knowledge Distillation. ICLR 2024 - [c32]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:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. ICML 2024 - [c31]Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara:
SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning. ICML 2024 - [i36]Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron C. Courville, Alessandro Sordoni, Rishabh Agarwal:
V-STaR: Training Verifiers for Self-Taught Reasoners. CoRR abs/2402.06457 (2024) - [i35]Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou:
Transformers Can Achieve Length Generalization But Not Robustly. CoRR abs/2402.09371 (2024) - [i34]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:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. CoRR abs/2403.03950 (2024) - [i33]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:
Many-Shot In-Context Learning. CoRR abs/2404.11018 (2024) - [i32]Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara:
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning. CoRR abs/2406.15025 (2024) - [i31]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:
On scalable oversight with weak LLMs judging strong LLMs. CoRR abs/2407.04622 (2024) - [i30]Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn:
Don't Throw Away Data: Better Sequence Knowledge Distillation. CoRR abs/2407.10456 (2024) - [i29]Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal:
Generative Verifiers: Reward Modeling as Next-Token Prediction. CoRR abs/2408.15240 (2024) - [i28]Hritik Bansal, Arian Hosseini, Rishabh Agarwal, Vinh Q. Tran, Mehran Kazemi:
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling. CoRR abs/2408.16737 (2024) - [i27]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:
Training Language Models to Self-Correct via Reinforcement Learning. CoRR abs/2409.12917 (2024) - 2023
- [c30]Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. AISTATS 2023: 1703-1718 - [c29]Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare:
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. ICLR 2023 - [c28]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR 2023 - [c27]Charline Le Lan, Rishabh Agarwal:
Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric. Tiny Papers @ ICLR 2023 - [c26]Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare:
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. ICLR 2023 - [c25]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. ICML 2023: 18686-18713 - [c24]Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. ICML 2023: 30365-30380 - [c23]Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci:
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. ICML 2023: 32145-32168 - [c22]Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok:
Revisiting Bellman Errors for Offline Model Selection. ICML 2023: 43369-43406 - [c21]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:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. NeurIPS 2023 - [i26]Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok:
Revisiting Bellman Errors for Offline Model Selection. CoRR abs/2302.00141 (2023) - [i25]Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci:
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. CoRR abs/2302.12902 (2023) - [i24]Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare:
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. CoRR abs/2304.12567 (2023) - [i23]Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. CoRR abs/2305.19452 (2023) - [i22]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. CoRR abs/2306.10171 (2023) - [i21]Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem:
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR abs/2306.13649 (2023) - [i20]Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal:
DistillSpec: Improving Speculative Decoding via Knowledge Distillation. CoRR abs/2310.08461 (2023) - [i19]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:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. CoRR abs/2310.08710 (2023) - [i18]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:
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR abs/2311.17894 (2023) - [i17]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:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR abs/2312.06585 (2023) - 2022
- [c20]Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon:
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. AAAI 2022: 7886-7894 - [c19]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. AISTATS 2022: 4132-4157 - [c18]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR 2022 - [c17]Jayantrao Mohite, Suryakant A. Sawant, Rishabh Agarwal, Ankur Pandit, Srinivasu Pappula:
Detection Of Crop Water Stress In Maize Using Drone Based Hyperspectral Imaging. IGARSS 2022: 5957-5960 - [c16]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS 2022 - [i16]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. CoRR abs/2203.00543 (2022) - [i15]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR abs/2206.01626 (2022) - [i14]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR abs/2211.15144 (2022) - [i13]Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. CoRR abs/2212.04025 (2022) - 2021
- [c15]Suryakant A. Sawant, Rishabh Agarwal, Jayantrao Mohite, Ankur Pandit, Srinivasu Pappula:
Field Boundary Identification using Convolutional Neural Network and GIS on High Resolution Satellite Observations. Agro-Geoinformatics 2021: 1-6 - [c14]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR 2021 - [c13]Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine:
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. ICLR 2021 - [c12]Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS 2021: 4699-4711 - [c11]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. NeurIPS 2021: 29304-29320 - [i12]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR abs/2101.05265 (2021) - [i11]Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon:
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. CoRR abs/2106.03273 (2021) - [i10]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR abs/2108.13264 (2021) - [i9]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR abs/2112.04716 (2021) - 2020
- [c10]Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi:
An Optimistic Perspective on Offline Reinforcement Learning. ICML 2020: 104-114 - [c9]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. ICML 2020: 3061-3071 - [c8]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:
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS 2020 - [c7]Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi:
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. SemEval@COLING 2020: 1665-1670 - [i8]Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR abs/2004.13912 (2020) - [i7]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) - [i6]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. CoRR abs/2007.06700 (2020) - [i5]Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi:
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. CoRR abs/2007.10820 (2020) - [i4]Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine:
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. CoRR abs/2010.14498 (2020)
2010 – 2019
- 2019
- [c6]Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi:
Learning to Generalize from Sparse and Underspecified Rewards. ICML 2019: 130-140 - [c5]Rishabh Agarwal, Sarah Bergbreiter:
Measurement of shear forces during gripping tasks with a low-cost tactile sensing system. RoboSoft 2019: 330-336 - [i3]Rishabh Agarwal:
Evaluation Function Approximation for Scrabble. CoRR abs/1901.08728 (2019) - [i2]Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi:
Learning to Generalize from Sparse and Underspecified Rewards. CoRR abs/1902.07198 (2019) - [i1]Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi:
Striving for Simplicity in Off-policy Deep Reinforcement Learning. CoRR abs/1907.04543 (2019) - 2017
- [c4]Ayush Shukla, Rishabjit Singh, Rishabh Agarwal, Muhammad Suhail, Subir K. Saha, Santanu Chaudhury:
Development of a Low-Cost Education Platform: RoboMuse 4.0. AIR 2017: 38:1-38:6 - [c3]Prakhar Gupta, Rishabh Agarwal, Surbhi Saraswat, Hari Prabhat Gupta, Tanima Dutta:
S-Pencil: A Smart Pencil Grip Monitoring System for Kids Using Sensors. GLOBECOM 2017: 1-6 - [c2]Manoj K. Raut, Tushar V. Kokane, Rishabh Agarwal:
Computing Theory Prime Implicates in Modal Logic. ISDA 2017: 273-282 - 2016
- [c1]Rishabh Agarwal, Prayag Sharma, Subir K. Saha, Takafumi Matsumaru:
Touchless human-mobile robot interaction using a projectable interactive surface. SII 2016: 723-728
Coauthor Index
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