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Yann Dubois
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2020 – today
- 2024
- [c13]Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto:
Identifying the Risks of LM Agents with an LM-Emulated Sandbox. ICLR 2024 - [i14]Yann Dubois, Balázs Galambosi, Percy Liang, Tatsunori B. Hashimoto:
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators. CoRR abs/2404.04475 (2024) - [i13]Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, Tatsunori Hashimoto, Carlos Guestrin:
Learning to (Learn at Test Time): RNNs with Expressive Hidden States. CoRR abs/2407.04620 (2024) - 2023
- [c12]Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori Hashimoto:
Is a Caption Worth a Thousand Images? A Study on Representation Learning. ICLR 2023 - [c11]Yann Dubois, Tatsunori Hashimoto, Percy Liang:
Evaluating Self-Supervised Learning via Risk Decomposition. ICML 2023: 8779-8820 - [c10]Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML 2023: 24720-24736 - [c9]Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto:
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. NeurIPS 2023 - [i12]Yann Dubois, Tatsunori Hashimoto, Percy Liang:
Evaluating Self-Supervised Learning via Risk Decomposition. CoRR abs/2302.03068 (2023) - [i11]Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto:
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. CoRR abs/2305.14387 (2023) - [i10]Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto:
Identifying the Risks of LM Agents with an LM-Emulated Sandbox. CoRR abs/2309.15817 (2023) - 2022
- [c8]Yangjun Ruan, Yann Dubois, Chris J. Maddison:
Optimal Representations for Covariate Shift. ICLR 2022 - [c7]Yann Dubois, Stefano Ermon, Tatsunori B. Hashimoto, Percy Liang:
Improving Self-Supervised Learning by Characterizing Idealized Representations. NeurIPS 2022 - [i9]Yangjun Ruan, Yann Dubois, Chris J. Maddison:
Optimal Representations for Covariate Shift. CoRR abs/2201.00057 (2022) - [i8]Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Data Augmentations. CoRR abs/2206.00051 (2022) - [i7]Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori Hashimoto:
Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning. CoRR abs/2207.07635 (2022) - [i6]Yann Dubois, Tatsunori Hashimoto, Stefano Ermon, Percy Liang:
Improving Self-Supervised Learning by Characterizing Idealized Representations. CoRR abs/2209.06235 (2022) - 2021
- [c6]Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison:
Lossy Compression for Lossless Prediction. NeurIPS 2021: 14014-14028 - [i5]Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison:
Lossy Compression for Lossless Prediction. CoRR abs/2106.10800 (2021) - 2020
- [c5]Yann Dubois, Gautier Dagan, Dieuwke Hupkes, Elia Bruni:
Location Attention for Extrapolation to Longer Sequences. ACL 2020: 403-413 - [c4]Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner:
Convolutional Conditional Neural Processes. ICLR 2020 - [c3]Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam:
Learning Optimal Representations with the Decodable Information Bottleneck. NeurIPS 2020 - [c2]Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner:
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. NeurIPS 2020 - [i4]Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner:
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. CoRR abs/2007.01332 (2020) - [i3]Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam:
Learning Optimal Representations with the Decodable Information Bottleneck. CoRR abs/2009.12789 (2020)
2010 – 2019
- 2019
- [i2]Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner:
Convolutional Conditional Neural Processes. CoRR abs/1910.13556 (2019) - [i1]Yann Dubois, Gautier Dagan, Dieuwke Hupkes, Elia Bruni:
Location Attention for Extrapolation to Longer Sequences. CoRR abs/1911.03872 (2019) - 2016
- [c1]Thibault Arloing, Yann Dubois, Stéphane Ducasse, Damien Cassou:
Pillar: A Versatile and Extensible Lightweight Markup Language. IWST 2016: 25
Coauthor Index
aka: Tatsunori Hashimoto
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