K
Kyle Hsu
Researcher at University of Toronto
Publications - 26
Citations - 742
Kyle Hsu is an academic researcher from University of Toronto. The author has contributed to research in topics: System identification & Control theory. The author has an hindex of 13, co-authored 23 publications receiving 590 citations. Previous affiliations of Kyle Hsu include University of California, Berkeley & Max Planck Society.
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Unsupervised Learning via Meta-Learning
TL;DR: This work develops an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data, and acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks.
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On the Opportunities and Risks of Foundation Models.
Rishi Bommasani,Drew A. Hudson,Ehsan Adeli,Russ B. Altman,Simran Arora,Sydney von Arx,Michael S. Bernstein,Jeannette Bohg,Antoine Bosselut,Emma Brunskill,Erik Brynjolfsson,Shyamal Buch,Dallas Card,Rodrigo Castellon,Niladri S. Chatterji,Annie Chen,Kathleen Creel,Jared Davis,Dora Demszky,Chris Donahue,Moussa Doumbouya,Esin Durmus,Stefano Ermon,John Etchemendy,Kawin Ethayarajh,Li Fei-Fei,Chelsea Finn,Trevor Gale,Lauren Gillespie,Karan Goel,Noah D. Goodman,Shelby Grossman,Neel Guha,Tatsunori Hashimoto,Peter Henderson,John Hewitt,Daniel E. Ho,Jenny Hong,Kyle Hsu,Jing Huang,Thomas Icard,Saahil Jain,Dan Jurafsky,Pratyusha Kalluri,Siddharth Karamcheti,Geoff Keeling,Fereshte Khani,Omar Khattab,Pang Wei Koh,Mark Krass,Ranjay Krishna,Rohith Kuditipudi,Ananya Kumar,Faisal Ladhak,Mina Lee,Tony Lee,Jure Leskovec,Isabelle Levent,Xiang Lisa Li,Xuechen Li,Tengyu Ma,Ali Ahmad Malik,Christopher D. Manning,Suvir Mirchandani,Eric Mitchell,Zanele Munyikwa,Suraj Nair,Avanika Narayan,Deepak Narayanan,Ben Newman,Allen Nie,Juan Carlos Niebles,Hamed Nilforoshan,Julian Nyarko,Giray Ogut,Laurel Orr,Isabel Papadimitriou,Joon Sung Park,Chris Piech,Eva Portelance,Christopher Potts,Aditi Raghunathan,Rob Reich,Hongyu Ren,Frieda Rong,Yusuf H. Roohani,Camilo Ruiz,Jack Ryan,Christopher Ré,Dorsa Sadigh,Shiori Sagawa,Keshav Santhanam,Andy Shih,Krishnan Srinivasan,Alex Tamkin,Rohan Taori,Armin W. Thomas,Florian Tramèr,Rose E. Wang,William Yang Wang,Bohan Wu,Jiajun Wu,Yuhuai Wu,Sang Michael Xie,Michihiro Yasunaga,Jiaxuan You,Matei Zaharia,Michael Zhang,Tianyi Zhang,Xikun Zhang,Yuhui Zhang,Lucia Zheng,Kaitlyn Zhou,Percy Liang +113 more
TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.
Proceedings Article
Unsupervised Learning via Meta-Learning
TL;DR: In this article, the authors develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from a small amount of unlabeled data.
Proceedings ArticleDOI
Multi-Layered Abstraction-Based Controller Synthesis for Continuous-Time Systems
TL;DR: It is empirically demonstrate that multi-layered synthesis can outperform standard (single-layer) ABCS algorithms on a number of examples, despite the additional cost of constructing multiple abstract systems.
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On the role of data in PAC-Bayes bounds.
TL;DR: This work shows that the bound based on the oracle prior can be suboptimal, and applies this new principle in the setting of nonconvex learning, simulating data-dependent oracle priors on MNIST and Fashion MNIST with and without held-out data, and demonstrating new nonvacuous bounds in both cases.