Learn more about blocking users. Thompson sampling #�;���$���J�Y����n"@����)|��Ϝ�L�?��!�H�&� ��D����@ %BHa�`�Ef�I�S��E�� �T >> Tatsunori Hashimoto (Stanford University, post-doc, jointly supervised with Percy Liang, 2016{2019). [Please refer to, Mon 10/29: Lecture 11: Total variation distance, Wasserstein distance, Wasserstein GANs How can we explain the predictions of a black-box model? Ms. Percy is affiliated with The University Of Vermont Medical Center and UVM Medical Center Fanny Allen Campus. Percy Liang (Preferred) Suggest Name; Emails. real analysis, Fp(t�� ��%4@@G���q�\ K�i���,% `) �Ԑ̀dR�i��t�o �l�Rl�M$Z�Ѱ��$1�)֔hXG���e*5�I��'�I��Rf2Gradgo"�4���h@E #- R x�-<>�)+��3e�M��t�`� One way to create this predictability is by taking advantage of machine learning. Percy Liang, Assistant Professor of Computer Science at Stanford University, explains that humans rely on some degree of predictability in their day-to-day interactions — both with other humans and automated systems (including, but not limited to, their cars). Search Search. Peter Bartlett's statistical learning theory course. Eng.) xڥW�r�6}�W�����$;�t\7�N�c��_ �0�������H'�cStg, g���]��"�IEdH�(1$""#�HĚ�RI"!��HI� [, Mon 12/03: Lecture 19: Regret bound for UCB, Bayesian setup, Bio. The best result we found for your search is Percy Shuo Liang age 30s in Stanford, CA in the Stanford neighborhood. … BAD GOOD. Articles Cited by. probability theory, Title. 2 0 obj << endobj AI Frontiers Conference brings together AI thought leaders to … You can help! My goal is to develop trustworthy systems that can communicate effectively with people and improve over time through interaction. endstream Research Areas. Sort by citations Sort by year Sort by title. Percy Liang percyliang. 378 0 obj << Percy Liang. Output: for each word type, its cluster (see output.txt for an example). Thompson Sampling from MIT, 2004; Ph.D. from UC Berkeley, 2011). [, Mon 10/15: Lecture 7: Rademacher complexity, neural networks His research spans theoretical machine learning to practical natural language processing; topics include semantic parsing, question answering, machine translation, online learning, method of moments, approximate inference, Bayesian modeling, and deep learning. Associate Professor of Computer Science, Stanford University. %PDF-1.5 Percy Liang Author page based on publicly available paper data. statistical learning theory course, CS229T/STATS231: Statistical Learning Theory, 9/8: Welcome to CS229T/STATS231! Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan * 1Sang Michael Xie Fanny Yang2 John C. Duchi 1Percy Liang … of Electrical Engineering and Computer Science, 2005. If you identify any major omissions or other inaccuracies in the publication list, please let us know. [, Mon 11/26: Lecture 17: Multi-armed bandit problem, general OCO with partial observation � �T ��f��Ej͏���8���H��8f�@��)���@���D���W�a�\ ��G@Nb���� ��P� 53. papers with code. Percy Liang. /Length 1337 online learning �R�[���8���ʵHaQ�W�ǁl�S����}�֓����]�HF��C#�F���/K����+��֮������#�I'ꉞ�'TcϽ�G�\�7�����-��m��}�;G����6�?�paC��i\�W.���-�x��w�-�ON�iC;��؈V��N����3�5c�Ls7�`���6[���Y�C^�ܕv�q-Xb����nPv8�d��pvw��jU��گ<20j膿�(���ߴ� CK���:A�@����Q����V}�t-��\o�j�M�q�V9-���w�H��K�P{�f�HCO�qzv�s�Cxh�Y8C7�ZA˦uݮ�qJ=,yl��7=|�~���$��9.F7.�Dxz��;��G�V���8|�[˝�U�q�:G|N��G/�ӈzLb��y�������Qh�j���w�{�{ �Ptƛi�x؋TLB�S�~�Ɇx��)��N|��a�OϾ{ ��DJ�O{��`�f �|�`��j7c&aƫO�$�9{���q�C�/��]�^��t�����/���� Block user Report abuse. stochastic setting Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. %���� [, Wed 10/17: Lecture 8: Margin-based generalization error of His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. This is Me - Control Profile. Percy Liang is this you? from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Percy Liang. x���o�6���+t��Z��.CV��=�;02c���#M�חI�q�6Z���N�h�����%-#�y��6��5d�)��D��H�qq�SL�"��. Skip slideshow. Percy is related to Zuyang L Liang and Ling Zhang as well as 1 additional person. View the profiles of people named Percy Liang. Cited by. Search for Percy Liang's work. statistical learning theory course, Martin Wainwright's Associate Professor of Computer Science and Statistics (courtesy) Artificial Intelligence Lab Natural Language Processing Group Statistical Machine Learning Group. machine learning natural language processing. Percy Liang Release 1.3 2012.07.24 Input: a sequence of words separated by whitespace (see input.txt for an example). Percy Liang. There is no required text for the course. [, Mon 10/22: Lecture 9: VC dimension, covering techniques stream from MIT, 2004; Ph.D. from UC Berkeley, 2011). Assistant Professor of Computer Science, Stanford University. /Filter /FlateDecode Cited by. How Should We Evaluate Machine Learning for AI? from MIT, 2004; Ph.D. from UC Berkeley, 2011). Select this result to view Percy Shuo Liang's phone number, address, and more. Percy Liang, 37 Palo Alto, CA. hypothesis class [, Wed 10/03: Lecture 4: naive epsilon-cover argument, concentration inequalities [, Wed 11/28: Lecture 18: Multi-armed bandit problem in the offerings of this course, Peter Bartlett's statistical learning theory course, Boyd and CS229T/STAT231: Statistical Learning Theory (Winter 2016) Percy Liang Last updated Wed Apr 20 2016 01:36 These lecture notes will be updated periodically as the course goes on. [, Wed 10/31: Lecture 12: Generalization and approximation in Sort. As part of the Trustworthy ML Initiative's seminar series, Percy Liang (Stanford University) presents "Surprises in the Quest for Robust Machine Learning". A number of useful references: Percy Liang's course notes from previous >> [, Wed 12/05: Lecture 20: Information theory, regret bound for [, Wed 10/24: Lecture 10: Covering techniques, overview of GANs Gates 250 / pliang@cs.stanford.edu [Publications] Research. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. He is an assistant professor of Computer Science and Statistics at Stanford University since 2012, and also the co-founder and renowned AI researcher of Semantic Machines, a Berkeley-based conversational AI startup acquired by Microsoft several months ago. 0.00 5.00 /5. BAD 1 - 2 POOR 2 - 3 FAIR 3 - 4 GOOD 4 - 5. 24. results. /First 813 Year; Squad: 100,000+ questions for machine comprehension of text. linear algebra, Percy Liang's 133 research works with 5,234 citations and 3,995 reads, including: Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning /Length 1467 �8YX�.��?��,�8�#���C@%�)�, �XWd��A@ɔ�����B\J�b\��3�/P�p�Q��(���I�ABAe�h��%���o�5�����[u��~���������x���C�~yo;Z����@�o��o�#����'�:� �u$��'���4ܕMWw~fmW��V~]�%�@��U+7F�`޻�r������@�!�U�+G��m��I�a��,]����Ҳ�,�!��}���.�-��4H����+Wu����/��Z9�3qno}ٗ��n�i}��M�f��l[T���K B�Qa;�Onl���e����`�$~���o]N���". Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Percy Liang, Computer Science Department, Stanford University/Statistics Department, Stanford University, My goal is to develop trustworthy systems that can communicate effectively with people and improve over time through interac. Martin Wainwright's statistical learning theory course [. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Overview, reviews, and comments on Percy Liang, mTurk Requester. Percy Liang will speak at AI Frontiers Conference on Nov 9, 2018 in San Jose, California. 10, 2012 Percy Liang Google/Stanford Block or report user Block or report percyliang. Verified email at cs.stanford.edu - Homepage. Associate Professor of Computer Science and Statistics. Percy Liang Thesis (M. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Featured Co-authors. /N 100 Reputation Score. Boyd and Vandenberghe's Convex Optimization. stream In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Wassersetin GANs [, Wed 11/07: Lecture 14: Online learning, online convex optimization, Follow the Leader (FTL) algorithm Percy Liang. Previous years' home pages are, Uniform convergence (VC dimension, Rademacher complexity, etc), Implicit/algorithmic regularization, generalization theory for neural networks, Unsupervised learning: exponential family, method of moments, statistical theory of GANs, A solid background in Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Join Facebook to connect with Percy Liang and others you may know. 122. papers. Prevent this user from interacting with your repositories and sending you notifications. Understanding and mitigating the tradeoff between robustness and accuracy.Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang.arXiv preprint arXiv:2002.10716, 2020. [, Wed 10/10: Lecture 6: Rademacher complexity, margin theory Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Includes bibliographical references (p. 75-82). two-layer neural networks [, Mon 10/08: Lecture 5: Sub-Gaussian random variables, Rademacher complexity Home Percy Liang. We are testing a new system for linking publications to authors. and, Machine learning (CS229) or statistics (STATS315A), Convex optimization (EE364A) is recommended, Mon 09/24: Lecture 1: overview, formulation of prediction /Type /ObjStm They have also lived in Palo Alto, CA and Berkeley, CA. Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. Approximate Reputation Score. No matching publications found. Ms. Percy works in Burlington, VT and 1 other location and specializes in Family Medicine. Block user. Vandenberghe's Convex Optimization, Sham Kakade's Follow. Learning Dependency-Based Compositional Semantics Semantic Representations for Textual Inference Workshop – Mar. --Massachusetts Institute of Technology, Dept. Percy Liang's course notes from previous offerings of this course. Percy Liang. [, Mon 11/12: Lecture 15: Follow the Regularized Leader (FTRL) algorithm : Percy Liang Home Research-feed … Bio Associate Professor in CS @Stanford @stanfordnlp | Pianist Lokasyon Stanford, CA Tweets 11 Followers 2,7K Following 197 Account created 31-10-2009 07:26:37 ID 86481377. Approx. Michael I. Jordan 185 publications . Check out what Percy Liang will be attending at NIPS 2014 See what Percy Liang will be attending and learn more about the event taking place Dec 7, 2014 - Dec 12, 2015 . His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to … If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. /Filter /FlateDecode His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Implementation of the Brown hierarchical word clustering algorithm. View the profiles of professionals named "Percy Liang" on LinkedIn. Jian Zhang 113 publications . Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. [, Thu 11/01: Homework 2 (uniform convergence), Mon 11/05: Lecture 13: Restricted Approximability, overview of problems, error decomposition [, Wed 09/26: Lecture 2: asymptotics of maximum likelihood estimators (MLE) [, Mon 10/01: Lecture 3: uniform convergence overview, finite Current Students and Postdoctoral Researchers Fanny Yang (Stanford University, post-doc, jointly supervised with Percy Liang, 2019). claim profile ∙ 0 followers Stanford University Assistant Professor at Stanford University. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments. Rate Percy. Free Instagram Followers [, Wed 11/14: Lecture 16: FTRL in concrete problems: online regression & expert problem, convex to linear reduction … Photos | Summary | Follow. Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. 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