[Please refer to, Mon 10/29: Lecture 11: Total variation distance, Wasserstein distance, Wasserstein GANs … 122. papers. [, Mon 10/08: Lecture 5: Sub-Gaussian random variables, Rademacher complexity problems, error decomposition [, Wed 09/26: Lecture 2: asymptotics of maximum likelihood estimators (MLE) [, Mon 10/01: Lecture 3: uniform convergence overview, finite Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan * 1Sang Michael Xie Fanny Yang2 John C. Duchi 1Percy Liang … endstream Percy Liang. hypothesis class [, Wed 10/03: Lecture 4: naive epsilon-cover argument, concentration inequalities Percy Liang. [, Mon 10/22: Lecture 9: VC dimension, covering techniques : Percy Liang [, Wed 10/24: Lecture 10: Covering techniques, overview of GANs This is Me - Control Profile. [, Mon 12/03: Lecture 19: Regret bound for UCB, Bayesian setup, Join Facebook to connect with Percy Liang and others you may know. Percy is related to Zuyang L Liang and Ling Zhang as well as 1 additional person. Prevent this user from interacting with your repositories and sending you notifications. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Percy Liang's course notes from previous offerings of this course. 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 11/28: Lecture 18: Multi-armed bandit problem in the /Filter /FlateDecode Articles Cited by. x���o�6���+t��Z��.CV��=�;02c���#M�חI�q�6Z���N�h�����%-#�y��6��5d�)��D��H�qq�SL�"��. 24. results. 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. Ms. Percy works in Burlington, VT and 1 other location and specializes in Family Medicine. Implementation of the Brown hierarchical word clustering algorithm. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Output: for each word type, its cluster (see output.txt for an example). Cited by. 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. Percy Liang. 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. Peter Bartlett's statistical learning theory course. xڥW�r�6}�W�����$;�t\7�N�c��_ �0�������H'�cStg, g���]��"�IEdH�(1$""#�HĚ�RI"!��HI� Vandenberghe's Convex Optimization, Sham Kakade's If you identify any major omissions or other inaccuracies in the publication list, please let us know. Free Instagram Followers [, Wed 11/07: Lecture 14: Online learning, online convex optimization, Follow the Leader (FTL) algorithm [, Mon 11/26: Lecture 17: Multi-armed bandit problem, general OCO with partial observation Approx. two-layer neural networks [, Wed 10/31: Lecture 12: Generalization and approximation in How Should We Evaluate Machine Learning for AI? from MIT, 2004; Ph.D. from UC Berkeley, 2011). Block or report user Block or report percyliang. from MIT, 2004; Ph.D. from UC Berkeley, 2011). %PDF-1.5 and, Machine learning (CS229) or statistics (STATS315A), Convex optimization (EE364A) is recommended, Mon 09/24: Lecture 1: overview, formulation of prediction No matching publications found. [, Wed 10/10: Lecture 6: Rademacher complexity, margin theory [, Mon 11/12: Lecture 15: Follow the Regularized Leader (FTRL) algorithm BAD GOOD. 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). Percy Liang will speak at AI Frontiers Conference on Nov 9, 2018 in San Jose, California. 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 � �T ��f��Ej͏���8���H��8f�@��)���@���D���W�a�\ ��G@Nb���� ��P� 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. One way to create this predictability is by taking advantage of machine learning. Search for Percy Liang's work. Thompson Sampling machine learning natural language processing. 2 0 obj << … �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���". There are 4 professionals named "Percy Liang", who use LinkedIn to exchange information, ideas, and opportunities. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. /Type /ObjStm Cited by. Percy Liang, 37 Palo Alto, CA. probability theory, Jian Zhang 113 publications . [, Mon 10/15: Lecture 7: Rademacher complexity, neural networks >> Sort by citations Sort by year Sort by title. 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�`� Associate Professor of Computer Science and Statistics (courtesy) Artificial Intelligence Lab Natural Language Processing Group Statistical Machine Learning Group. /First 813 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 0.00 5.00 /5. /N 100 /Filter /FlateDecode Featured Co-authors. Wassersetin GANs 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 . endobj View the profiles of professionals named "Percy Liang" on LinkedIn. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Thompson sampling online learning Martin Wainwright's statistical learning theory course 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. Home Percy Liang. Eng.) How can we explain the predictions of a black-box model? Home Research-feed … Percy Liang is this you? real analysis, 53. papers with code. Year; Squad: 100,000+ questions for machine comprehension of text. 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. A number of useful references: Percy Liang's course notes from previous /Length 1467 If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments. You can help! Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. Fanny Yang (Stanford University, post-doc, jointly supervised with Percy Liang, 2019). stream [, Thu 11/01: Homework 2 (uniform convergence), Mon 11/05: Lecture 13: Restricted Approximability, overview of stochastic setting Select this result to view Percy Shuo Liang's phone number, address, and more. Percy Liang. Gates 250 / pliang@cs.stanford.edu [Publications] Research. Photos | Summary | Follow. >> 378 0 obj << Approximate Reputation Score. They have also lived in Palo Alto, CA and Berkeley, CA. Assistant Professor of Computer Science, Stanford University. Rate Percy. Bio. �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�����/���� Tatsunori Hashimoto (Stanford University, post-doc, jointly supervised with Percy Liang, 2016{2019). Learning Dependency-Based Compositional Semantics Semantic Representations for Textual Inference Workshop – Mar. Sham Kakade's statistical learning theory course. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Reputation Score. Learn more about blocking users. Sort. stream Percy Liang Thesis (M. %���� [, Wed 12/05: Lecture 20: Information theory, regret bound for Michael I. Jordan 185 publications . Includes bibliographical references (p. 75-82). AI Frontiers Conference brings together AI thought leaders to … Associate Professor of Computer Science and Statistics. My goal is to develop trustworthy systems that can communicate effectively with people and improve over time through interaction. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. The best result we found for your search is Percy Shuo Liang age 30s in Stanford, CA in the Stanford neighborhood. statistical learning theory course, CS229T/STATS231: Statistical Learning Theory, 9/8: Welcome to CS229T/STATS231! 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. Associate Professor of Computer Science, Stanford University. 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. Percy Liang percyliang. Fp(t�� ��%4@@G���q�\ claim profile ∙ 0 followers Stanford University Assistant Professor at Stanford University. Research Areas. Current Students and Postdoctoral Researchers 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. offerings of this course, Peter Bartlett's statistical learning theory course, Boyd and #�;���$���J�Y����n"@����)|��Ϝ�L�?��!�H�&� ��D����@ %BHa�`�Ef�I�S��E�� �T Verified email at cs.stanford.edu - Homepage. Percy Liang Author page based on publicly available paper data. We are testing a new system for linking publications to authors. of Electrical Engineering and Computer Science, 2005. linear algebra, Title. Percy Liang. Block user Report abuse. Search Search. As part of the Trustworthy ML Initiative's seminar series, Percy Liang (Stanford University) presents "Surprises in the Quest for Robust Machine Learning". --Massachusetts Institute of Technology, Dept. Ms. Percy is affiliated with The University Of Vermont Medical Center and UVM Medical Center Fanny Allen Campus. View the profiles of people named Percy Liang. Overview, reviews, and comments on Percy Liang, mTurk Requester. Block user. [, Wed 10/17: Lecture 8: Margin-based generalization error of Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 10, 2012 Percy Liang Google/Stanford Boyd and Vandenberghe's Convex Optimization. Follow. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to … /Length 1337 [. Percy Liang (Preferred) Suggest Name; Emails. There is no required text for the course. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Percy Liang. [, Wed 11/14: Lecture 16: FTRL in concrete problems: online regression & expert problem, convex to linear reduction statistical learning theory course, Martin Wainwright's His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Percy Liang Release 1.3 2012.07.24 Input: a sequence of words separated by whitespace (see input.txt for an example). Skip slideshow. 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. BAD 1 - 2 POOR 2 - 3 FAIR 3 - 4 GOOD 4 - 5. 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