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Andy Shih
Researcher at University of California, Los Angeles
Publications - 17
Citations - 375
Andy Shih is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Probabilistic logic & Graphical model. The author has an hindex of 8, co-authored 17 publications receiving 253 citations. Previous affiliations of Andy Shih include Stanford University.
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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 ArticleDOI
A Symbolic Approach to Explaining Bayesian Network Classifiers.
TL;DR: In this article, the authors propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form.
Journal ArticleDOI
Compiling Bayesian Network Classifiers into Decision Graphs.
TL;DR: An algorithm is proposed for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the classifiers, which are tractable and can be exponentially smaller in size than decision trees.
Book ChapterDOI
Verifying Binarized Neural Networks by Angluin-Style Learning
TL;DR: An Angluin-style learning algorithm is proposed to compile a neural network on a given region into an Ordered Binary Decision Diagram (OBDD), using a SAT solver as an equivalence oracle to verify the behavior of binarized neural networks.
Proceedings ArticleDOI
On Tractable Representations of Binary Neural Networks.
TL;DR: A more efficient approach for compiling neural networks is considered, based on a pseudo-polynomial time algorithm for compiling a neuron, and it is shown that it is feasible to obtain compact representations of neural networks as SDDs.