Touchless human-mobile robot interaction using a projectable interactive surface. - Rishabh Agarwal; Prayag Sharma; Subir K. Saha; Takafumi Matsumaru (SII, 2016)

Development of a Low-Cost Education Platform: RoboMuse 4.0. - Ayush Shukla; Rishabjit Singh; Rishabh Agarwal; Muhammad Suhail; Subir K. Saha; Santanu Chaudhury (AIR, 2017)

S-Pencil: A Smart Pencil Grip Monitoring System for Kids Using Sensors. - Prakhar Gupta; Rishabh Agarwal; Surbhi Saraswat; Hari Prabhat Gupta; Tanima Dutta (GLOBECOM, 2017)

Computing Theory Prime Implicates in Modal Logic. - Manoj K. Raut; Tushar V. Kokane; Rishabh Agarwal (ISDA, 2017)

Learning to Generalize from Sparse and Underspecified Rewards. - Rishabh Agarwal; Chen Liang; Dale Schuurmans; Mohammad Norouzi 002 (ICML, 2019)

Measurement of shear forces during gripping tasks with a low-cost tactile sensing system. - Rishabh Agarwal; Sarah Bergbreiter (RoboSoft, 2019)

Evaluation Function Approximation for Scrabble. - Rishabh Agarwal (CoRR, 2019)

Striving for Simplicity in Off-policy Deep Reinforcement Learning. - Rishabh Agarwal; Dale Schuurmans; Mohammad Norouzi 002 (CoRR, 2019)

An Optimistic Perspective on Offline Reinforcement Learning. - Rishabh Agarwal; Dale Schuurmans; Mohammad Norouzi 002 (ICML, 2020)

Revisiting Fundamentals of Experience Replay. - William Fedus; Prajit Ramachandran; Rishabh Agarwal; Yoshua Bengio; Hugo Larochelle; Mark Rowland 001; Will Dabney (ICML, 2020)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Thomas Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (NeurIPS, 2020)

IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. - Vipul Singhal; Sahil Dhull; Rishabh Agarwal; Ashutosh Modi (SemEval@COLING, 2020)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Nicholas Frosst; Xuezhou Zhang; Rich Caruana; Geoffrey E. Hinton (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

Field Boundary Identification using Convolutional Neural Network and GIS on High Resolution Satellite Observations. - Suryakant A. Sawant; Rishabh Agarwal; Jayantrao Mohite; Ankur Pandit; Srinivasu Pappula (Agro-Geoinformatics, 2021)

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. - Rishabh Agarwal; Marlos C. Machado; Pablo Samuel Castro; Marc G. Bellemare (ICLR, 2021)

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. - Aviral Kumar; Rishabh Agarwal; Dibya Ghosh; Sergey Levine (ICLR, 2021)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Levi Melnick; Nicholas Frosst; Xuezhou Zhang; Benjamin J. Lengerich; Rich Caruana; Geoffrey E. Hinton (NeurIPS, 2021)

Deep Reinforcement Learning at the Edge of the Statistical Precipice. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2021)

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. - Evgenii Nikishin; Romina Abachi; Rishabh Agarwal; Pierre-Luc Bacon (AAAI, 2022)

On the Generalization of Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Adam Oberman; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 2022)

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. - Aviral Kumar; Rishabh Agarwal; Tengyu Ma 001; Aaron C. Courville; George Tucker; Sergey Levine (ICLR, 2022)

Detection Of Crop Water Stress In Maize Using Drone Based Hyperspectral Imaging. - Jayantrao Mohite; Suryakant A. Sawant; Rishabh Agarwal; Ankur Pandit; Srinivasu Pappula (IGARSS, 2022)

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2022)

Beyond Tabula Rasa: Reincarnating Reinforcement Learning. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (CoRR, 2022)

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. - Charline Le Lan; Joshua Greaves; Jesse Farebrother; Mark Rowland 001; Fabian Pedregosa; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 2023)

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. - Jesse Farebrother; Joshua Greaves; Rishabh Agarwal; Charline Le Lan; Ross Goroshin; Pablo Samuel Castro; Marc G. Bellemare (ICLR, 2023)

Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. - Aviral Kumar; Rishabh Agarwal; Xinyang Geng; George Tucker; Sergey Levine (ICLR, 2023)

Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric. - Charline Le Lan; Rishabh Agarwal (Tiny Papers @ ICLR, 2023)

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. - Adrien Ali Taïga; Rishabh Agarwal; Jesse Farebrother; Aaron C. Courville; Marc G. Bellemare (ICLR, 2023)

Bootstrapped Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Mark Rowland 001; Anna Harutyunyan; Rishabh Agarwal; Marc G. Bellemare; Will Dabney (ICML, 2023)

Bigger, Better, Faster: Human-level Atari with human-level efficiency. - Max Schwarzer; Johan S. Obando-Ceron; Aaron C. Courville; Marc G. Bellemare; Rishabh Agarwal; Pablo Samuel Castro (ICML, 2023)

The Dormant Neuron Phenomenon in Deep Reinforcement Learning. - Ghada Sokar; Rishabh Agarwal; Pablo Samuel Castro; Utku Evci (ICML, 2023)

Revisiting Bellman Errors for Offline Model Selection. - Joshua P. Zitovsky; Daniel de Marchi; Rishabh Agarwal; Michael Rene Kosorok (ICML, 2023)

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. - Cole Gulino; Justin Fu; Wenjie Luo; George Tucker; Eli Bronstein; Yiren Lu 001; Jean Harb; Xinlei Pan; Yan Wang 051; Xiangyu Chen 007; John D. Co-Reyes; Rishabh Agarwal; et al. (NeurIPS, 2023)

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. - Rishabh Agarwal; Nino Vieillard; Piotr Stanczyk; Sabela Ramos; Matthieu Geist; Olivier Bachem (CoRR, 2023)

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. - 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 (CoRR, 2023)

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. - Rishabh Agarwal; Nino Vieillard; Yongchao Zhou; Piotr Stanczyk; Sabela Ramos Garea; Matthieu Geist; Olivier Bachem (ICLR, 2024)

DistillSpec: Improving Speculative Decoding via Knowledge Distillation. - Yongchao Zhou; Kaifeng Lyu; Ankit Singh Rawat; Aditya Krishna Menon; Afshin Rostamizadeh; Sanjiv Kumar; Jean-François Kagy; Rishabh Agarwal (ICLR, 2024)

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. - 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 (ICML, 2024)

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning. - Matthias Weissenbacher; Rishabh Agarwal; Yoshinobu Kawahara (ICML, 2024)

Many-Shot In-Context Learning. - 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; et al. (NeurIPS, 2024)

On scalable oversight with weak LLMs judging strong LLMs. - 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 (NeurIPS, 2024)

V-STaR: Training Verifiers for Self-Taught Reasoners. - Arian Hosseini; Xingdi Yuan; Nikolay Malkin; Aaron C. Courville; Alessandro Sordoni; Rishabh Agarwal (CoRR, 2024)

Transformers Can Achieve Length Generalization But Not Robustly. - Yongchao Zhou; Uri Alon 002; Xinyun Chen; Xuezhi Wang 002; Rishabh Agarwal; Denny Zhou (CoRR, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (CoRR, 2024)

Don't Throw Away Data: Better Sequence Knowledge Distillation. - Jun Wang 126; Eleftheria Briakou; Hamid Dadkhahi; Rishabh Agarwal; Colin Cherry; Trevor Cohn (CoRR, 2024)

Not All LLM Reasoners Are Created Equal. - Arian Hosseini; Alessandro Sordoni; Daniel Toyama; Aaron C. Courville; Rishabh Agarwal (CoRR, 2024)

Evolving Alignment via Asymmetric Self-Play. - Ziyu Ye; Rishabh Agarwal; Tianqi Liu 002; Rishabh Joshi; Sarmishta Velury; Quoc V. Le; Qijun Tan; Yuan Liu (CoRR, 2024)

A comprehensive survey on answer generation methods using NLP. - Prashant Upadhyay; Rishabh Agarwal; Sumeet Dhiman; Abhinav Sarkar; Saumya Chaturvedi (Nat. Lang. Process. J., 2024)

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Avi Singh; John D. Co-Reyes; Rishabh Agarwal; Ankesh Anand; Piyush Patil; Xavier Garcia; Peter J. Liu; James Harrison; Jaehoon Lee 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling. - Hritik Bansal; Arian Hosseini; Rishabh Agarwal; Vinh Q. Tran 002; Mehran Kazemi (ICLR, 2025)

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models. - Yinlam Chow; Guy Tennenholtz; Izzeddin Gur; Vincent Zhuang; Bo Dai 001; Aviral Kumar; Rishabh Agarwal; Sridhar Thiagarajan; Craig Boutilier; Aleksandra Faust (ICLR, 2025)

Training Language Models to Self-Correct via Reinforcement Learning. - 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; et al. (ICLR, 2025)

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. - Michael Noukhovitch; Shengyi Huang; Sophie Xhonneux; Arian Hosseini; Rishabh Agarwal; Aaron C. Courville (ICLR, 2025)

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. - Amrith Setlur; Chirag Nagpal; Adam Fisch; Xinyang Geng; Jacob Eisenstein; Rishabh Agarwal; Alekh Agarwal; Jonathan Berant; Aviral Kumar (ICLR, 2025)

Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling. - Wenda Xu; Rujun Han; Zifeng Wang 002; Long T. Le; Dhruv Madeka; Lei Li 005; William Yang Wang; Rishabh Agarwal; Chen-Yu Lee; Tomas Pfister (ICLR, 2025)

Generative Verifiers: Reward Modeling as Next-Token Prediction. - Lunjun Zhang; Arian Hosseini; Hritik Bansal; Mehran Kazemi; Aviral Kumar; Rishabh Agarwal (ICLR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)

Process Reward Models That Think. - Muhammad Khalifa; Rishabh Agarwal; Lajanugen Logeswaran; Jaekyeom Kim; Hao Peng 009; Moontae Lee; Honglak Lee; Lu Wang 008 (CoRR, 2025)

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers. - Kusha Sareen; Morgane M. Moss; Alessandro Sordoni; Rishabh Agarwal; Arian Hosseini (CoRR, 2025)

Towards Compute-Optimal Many-Shot In-Context Learning. - Shahriar Golchin; Yanfei Chen; Rujun Han; Manan Gandhi; Tianli Yu; Swaroop Mishra; Mihai Surdeanu; Rishabh Agarwal; Chen-Yu Lee; Tomas Pfister (CoRR, 2025)