Sébastien M. R. Arnold

According to our database, Sébastien M. R. Arnold authored at least 16 papers between 2017 and 2024.

Collaborative distances:

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

2024

Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien M. R. Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu

CoRR, 2024

Can an LLM-Powered Socially Assistive Robot Effectively and Safely Deliver Cognitive Behavioral Therapy? A Study With University Students.

Mina J. Kian, Mingyu Zong, Katrin Fischer, Abhyuday Singh, Anna-Maria Velentza, Pau Sang, Shriya Upadhyay, Anika Gupta, Misha A. Faruki, Wallace Browning, Sébastien M. R. Arnold, Bhaskar Krishnamachari, Maja J. Mataric

CoRR, 2024

2023

A domain-agnostic approach for characterization of lifelong learning systems.

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 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

Neural Networks, March, 2023

Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL.

Sébastien M. R. Arnold, Fei Sha

CoRR, 2023

RoboCLIP: One Demonstration is Enough to Learn Robot Policies.

Sumedh Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, Laurent Itti

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Policy Learning and Evaluation with Randomized Quasi-Monte Carlo.

Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Embedding Adaptation is Still Needed for Few-Shot Learning.

Sébastien M. R. Arnold, Fei Sha

CoRR, 2021

Uniform Sampling over Episode Difficulty.

Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

When MAML Can Adapt Fast and How to Assist When It Cannot.

Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

learn2learn: A Library for Meta-Learning Research.

Sébastien M. R. Arnold, Praateek Mahajan, Debajyoti Datta, Ian Bunner, Konstantinos Saitas Zarkias

CoRR, 2020

2019

Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning.

Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha

CoRR, 2019

Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator.

James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei, Marius Kloft

CoRR, 2019

Reducing the variance in online optimization by transporting past gradients.

Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Shapechanger: Environments for Transfer Learning.

Sébastien M. R. Arnold, Tsam Kiu Pun, Théo-Tim J. Denisart, Francisco J. Valero Cuevas

CoRR, 2017

Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix.

Sébastien M. R. Arnold, Chunming Wang

CoRR, 2017

Accelerating SGD for Distributed Deep-Learning Using an Approximted Hessian Matrix.

Sébastien M. R. Arnold, Chunming Wang

Proceedings of the 5th International Conference on Learning Representations, 2017