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ORCIDSébastien M. R. Arnold
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Journal Articles
- 2023
[j1]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:
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160: 274-296 (2023)
Conference and Workshop Papers
- 2023
- [c6]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 - 2022
- [c5]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: 1041-1061 - 2021
- [c4]Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha:
When MAML Can Adapt Fast and How to Assist When It Cannot. AISTATS 2021: 244-252 - [c3]Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto:
Uniform Sampling over Episode Difficulty. NeurIPS 2021: 1481-1493 - 2019
- [c2]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: 5392-5403 - 2017
- [c1]Sébastien M. R. Arnold, Chunming Wang:
Accelerating SGD for Distributed Deep-Learning Using an Approximted Hessian Matrix. ICLR (Workshop) 2017
Informal and Other Publications
- 2024
- [i14]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:
Can an LLM-Powered Socially Assistive Robot Effectively and Safely Deliver Cognitive Behavioral Therapy? A Study With University Students. CoRR abs/2402.17937 (2024) - [i13]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:
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? CoRR abs/2406.13121 (2024) - 2023
- [i12]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 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 abs/2301.07799 (2023) - [i11]Sébastien M. R. Arnold, Fei Sha:
Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL. CoRR abs/2302.06009 (2023) - [i10]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 abs/2310.07899 (2023) - 2022
- [i9]Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha:
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. CoRR abs/2202.07808 (2022) - 2021
- [i8]Sébastien M. R. Arnold, Fei Sha:
Embedding Adaptation is Still Needed for Few-Shot Learning. CoRR abs/2104.07255 (2021) - [i7]Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran, Stefano Soatto:
Uniform Sampling over Episode Difficulty. CoRR abs/2108.01662 (2021) - 2020
- [i6]Sébastien M. R. Arnold, Praateek Mahajan, Debajyoti Datta, Ian Bunner, Konstantinos Saitas Zarkias:
learn2learn: A Library for Meta-Learning Research. CoRR abs/2008.12284 (2020) - 2019
- [i5]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 abs/1906.03532 (2019) - [i4]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 abs/1910.01249 (2019) - [i3]Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha:
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning. CoRR abs/1910.13603 (2019) - 2017
- [i2]Sébastien M. R. Arnold, Chunming Wang:
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix. CoRR abs/1709.05069 (2017) - [i1]Sébastien M. R. Arnold, Tsam Kiu Pun, Théo-Tim J. Denisart, Francisco J. Valero Cuevas:
Shapechanger: Environments for Transfer Learning. CoRR abs/1709.05070 (2017)
Coauthor Index
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