Saurabh Kumar

According to our database, Saurabh Kumar authored at least 12 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The Need for a Big World Simulator: A Scientific Challenge for Continual Learning.

Saurabh Kumar, Hong Jun Jeon, Alex Lewandowski, Benjamin Van Roy

CoRR, 2024

Satisficing Exploration for Deep Reinforcement Learning.

Dilip Arumugam, Saurabh Kumar, Ramki Gummadi, Benjamin Van Roy

CoRR, 2024

Learning Continually by Spectral Regularization.

Alex Lewandowski, Saurabh Kumar, Dale Schuurmans, András György, Marlos C. Machado

CoRR, 2024

2023

Maintaining Plasticity via Regenerative Regularization.

Saurabh Kumar, Henrik Marklund, Benjamin Van Roy

CoRR, 2023

Continual Learning as Computationally Constrained Reinforcement Learning.

Saurabh Kumar, Henrik Marklund, Ashish Rao, Yifan Zhu, Hong Jun Jeon, Yueyang Liu, Benjamin Van Roy

CoRR, 2023

2022

A Parametric Class of Approximate Gradient Updates for Policy Optimization.

Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans

Proceedings of the International Conference on Machine Learning, 2022

2021

Characterizing the Gap Between Actor-Critic and Policy Gradient.

Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Gradient Surgery for Multi-Task Learning.

Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL.

Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Statistics and Samples in Distributional Reinforcement Learning.

Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney

Proceedings of the 36th International Conference on Machine Learning, 2019

DeepMDP: Learning Continuous Latent Space Models for Representation Learning.

Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Dopamine: A Research Framework for Deep Reinforcement Learning.

Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare

CoRR, 2018