Rishabh Agarwal

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 67

Top Topics

Stochastic Gradient Descent

Reinforcement Learning

Top Venues

Publications

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)
Prashant Upadhyay, Rishabh Agarwal, Sumeet Dhiman, Abhinav Sarkar, Saumya Chaturvedi
A comprehensive survey on answer generation methods using NLP. Nat. Lang. Process. J. 8 (2024)
Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou
Transformers Can Achieve Length Generalization But Not Robustly. CoRR (2024)
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)
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 (2024)
Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning. CoRR (2024)
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 (2024)
Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron C. Courville, Alessandro Sordoni, Rishabh Agarwal
V-STaR: Training Verifiers for Self-Taught Reasoners. CoRR (2024)
Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn
Don't Throw Away Data: Better Sequence Knowledge Distillation. CoRR (2024)
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)
Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie C. Y. 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 (2024)
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 (2023)
Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok
Revisiting Bellman Errors for Offline Model Selection. ICML (2023)
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 (2023)
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. CoRR (2023)
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 (2023)
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 (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. CoRR (2023)
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 (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. ICML (2023)
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 (2023)
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)
Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok
Revisiting Bellman Errors for Offline Model Selection. CoRR (2023)
Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR (2023)
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)
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. ICML (2023)
Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. ICLR (2023)
Charline Le Lan, Rishabh Agarwal
Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric. Tiny Papers @ ICLR (2023)
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)
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)
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. ICLR (2023)
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. AAAI (2022)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS (2022)
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
On the Generalization of Representations in Reinforcement Learning. AISTATS (2022)
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR (2022)
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR (2022)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR (2022)
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
On the Generalization of Representations in Reinforcement Learning. CoRR (2022)
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 (2022)
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)
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR (2021)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR (2021)
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)
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)
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)
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR (2021)
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. ICLR (2021)
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR (2021)
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. CoRR (2021)
Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton
Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR (2020)
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
Revisiting Fundamentals of Experience Replay. ICML (2020)
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)
Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
An Optimistic Perspective on Offline Reinforcement Learning. ICML (2020)
Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. SemEval@COLING (2020)
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 (2020)
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. CoRR (2020)
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
Revisiting Fundamentals of Experience Replay. CoRR (2020)
Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. CoRR (2020)
Rishabh Agarwal, Sarah Bergbreiter
Measurement of shear forces during gripping tasks with a low-cost tactile sensing system. RoboSoft (2019)
Rishabh Agarwal
Evaluation Function Approximation for Scrabble. CoRR (2019)
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi
Learning to Generalize from Sparse and Underspecified Rewards. CoRR (2019)
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi
Learning to Generalize from Sparse and Underspecified Rewards. ICML (2019)
Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
Striving for Simplicity in Off-policy Deep Reinforcement Learning. CoRR (2019)
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)
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)
Manoj K. Raut, Tushar V. Kokane, Rishabh Agarwal
Computing Theory Prime Implicates in Modal Logic. ISDA (2017)
Rishabh Agarwal, Prayag Sharma, Subir K. Saha, Takafumi Matsumaru
Touchless human-mobile robot interaction using a projectable interactive surface. SII (2016)