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Sébastien M. R. Arnold
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2020 – today
- 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
- [j1]Megan M. Baker
, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160: 274-296 (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 - [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
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
- [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 - [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
- [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 - [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)
2010 – 2019
- 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 - [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
- [c1]Sébastien M. R. Arnold, Chunming Wang:
Accelerating SGD for Distributed Deep-Learning Using an Approximted Hessian Matrix. ICLR (Workshop) 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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