Sébastien M. R. Arnold

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 19

Top Topics

Uniform Sampling

Quasi Monte Carlo

Learning Systems

Top Venues

ICLR (Workshop)

Publications

Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR (2023)
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160 (2023)
Sumedh A. Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, Laurent Itti
RoboCLIP: One Demonstration is Enough to Learn Robot Policies. CoRR (2023)
Sumedh Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, Laurent Itti
RoboCLIP: One Demonstration is Enough to Learn Robot Policies. NeurIPS (2023)
Sébastien M. R. Arnold, Fei Sha
Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL. CoRR (2023)
Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. AISTATS (2022)
Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. CoRR (2022)
Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto
Uniform Sampling over Episode Difficulty. CoRR (2021)
Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto
Uniform Sampling over Episode Difficulty. NeurIPS (2021)
Sébastien M. R. Arnold, Fei Sha
Embedding Adaptation is Still Needed for Few-Shot Learning. CoRR (2021)
Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha
When MAML Can Adapt Fast and How to Assist When It Cannot. AISTATS (2021)
Sébastien M. R. Arnold, Praateek Mahajan, Debajyoti Datta, Ian Bunner, Konstantinos Saitas Zarkias
learn2learn: A Library for Meta-Learning Research. CoRR (2020)
James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei, Marius Kloft
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator. CoRR (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. NeurIPS (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. CoRR (2019)
Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning. CoRR (2019)
Sébastien M. R. Arnold, Tsam Kiu Pun, Théo-Tim J. Denisart, Francisco J. Valero Cuevas
Shapechanger: Environments for Transfer Learning. CoRR (2017)
Sébastien M. R. Arnold, Chunming Wang
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix. CoRR (2017)
Sébastien M. R. Arnold, Chunming Wang
Accelerating SGD for Distributed Deep-Learning Using an Approximted Hessian Matrix. ICLR (Workshop) (2017)