Spectral methods for learning latent­variable models (joint work with Daniel Hsu, Sham Kakade, Arun … /Filter /FlateDecode #�;���$���J�Y����n"@����)|��Ϝ�L�?��!�H�&� ��D����@ %BHa�`�Ef�I�S��E�� �T probability theory, Thompson sampling [, Mon 10/15: Lecture 7: Rademacher complexity, neural networks OpenURL . problems, error decomposition [, Wed 09/26: Lecture 2: asymptotics of maximum likelihood estimators (MLE) [, Mon 10/01: Lecture 3: uniform convergence overview, finite Abstract. online learning Percy Liang Computer Forum April 16, 2013 ... Summary so far: Modeling deep semantics of natural language is important Need to learn from natural/weak supervision to obtain broad coverage Rest of talk: Spectral methods for learning latent­variable models Learning a broad coverage semantic parser 11. /Filter /FlateDecode Vandenberghe's Convex Optimization, Sham Kakade's Amita Kamath Robin Jia Percy Liang Computer Science Department, Stanford University fkamatha, robinjia, pliangg@cs.stanford.edu Abstract To avoid giving wrong answers, question an-swering (QA) models need to know when to abstain from answering. … There is no required text for the course. /Type /ObjStm Deep vs. … stream Better bound? Liang, who went to high school in Arizona, has been playing piano since the age of eight and won … Summary; Citations; Active Bibliography; Co-citation; Clustered Documents; Version History; BibTeX @MISC{Chaganty_spectralexperts, author = {Arun Tejasvi Chaganty and Percy Liang}, title = {Spectral Experts for Estimating Mixtures of Linear Regressions}, year = {}} Share. percyliang has 12 repositories available. To scale up influence functions to modern machine learning … Compositional question answering begins by mapping questions to logical forms, but training a … In particular, I am interested in executable representations such as database queries or … xڥW�r�6}�W�����$;�t\7�N�c��_ �0�������H'�cStg, g���]��"�IEdH�(1$""#�HĚ�RI"!��HI� From notes of Percy Liang. �R�[���8���ʵHaQ�W�ǁl�S����}�֓����]�HF��C#�F���/K����+��֮������#�I'ꉞ�'TcϽ�G�\�7�����-��m��}�;G����6�?�paC��i\�W.���-�x��w�-�ON�iC;��؈V��N����3�5c�Ls7�`���6[���Y�C^�ܕv�q-Xb����nPv8�d��pvw��jU��گ<20j膿�(���ߴ� CK���:A�@����Q����V}�t-��\o�j�M�q�V9-���w�H��K�P{�f�HCO�qzv�s�Cxh�Y8C7�ZA˦uݮ�qJ=,yl��7=|�~���$��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�$�9{���q�C�/��]�^��t�����/���� 378 0 obj << /First 813 2 0 obj << Martin Wainwright's statistical learning theory course Wassersetin GANs In this paper, we use influence func-tions — a classic technique from robust statis-tics — to trace a model’s prediction through the learning algorithm and back to its training data, thereby identifying training points most respon-sible for a given prediction. /Length 1467 Thompson Sampling … Percy Liang ; Roweis and Saul ; Percy Liang ; Amos Storkey ; PCA : M. Girolami ; Andrew Ng ; Kevin Murphy ; Amos Storkey ; Lindsay Smith ; Kevin Murphy ; Model Selection: Topic Notes Slides Reading Homework; Model Selection/Comparison : Andrew Ng ; Zoubin Ghahramani ; Parameter estimation/Optimization techniques Topic Notes Slides Reading Homework; Parameter estimation : … >> Fp(t�� ��%4@@G���q�\ Runner up best paper. Project Summary. >> [, Wed 11/28: Lecture 18: Multi-armed bandit problem in the Percy Liang's course notes from previous offerings of this course. [, Wed 12/05: Lecture 20: Information theory, regret bound for stream K�i���,% `) �Ԑ̀dR�i��t�o �l�Rl�M$Z�Ѱ��$1�)֔hXG���e*5�I��'�I��Rf2Gradgo"�4���h@E #- R x�-<>�)+��3e�M��t�`� Pang Wei Koh 1Percy Liang Abstract How can we explain the predictions of a black-box model? [. [, Wed 10/24: Lecture 10: Covering techniques, overview of GANs hypothesis class [, Wed 10/03: Lecture 4: naive epsilon-cover argument, concentration inequalities NAACL 2019 (short … By eliminating variables and making existential quantification implicit, lambda DCS logical forms are generally more compact than those in lambda calculus. Uploaded By sttg6. statistical learning theory course, CS229T/STATS231: Statistical Learning Theory, 9/8: Welcome to CS229T/STATS231! Pages 12. Liang, a senior majoring in computer science and minoring in music and also a student in the Master of Engineering program, will present an Advanced Music Performance piano recital today (March 17) at 5 p.m. in Killian Hall. John Hewitt and Christopher D. Manning. Universality of NN. [, Wed 10/17: Lecture 8: Margin-based generalization error of [, Mon 11/26: Lecture 17: Multi-armed bandit problem, general OCO with partial observation and, Machine learning (CS229) or statistics (STATS315A), Convex optimization (EE364A) is recommended, Mon 09/24: Lecture 1: overview, formulation of prediction Percy Liang Associate Professor of Computer Science and Statistics (courtesy) How does it improve bound for various classes of functions? Moreover, users of- ten ask questions that diverge from the model’s training data, making errors more likely and thus abstention more critical. Lecture 7: MDPs I CS221: Articial Intelligence (Autumn 2013) - Percy Liang So far: search problems F B S D C E A state s, action a CS221: Summary; Citations; Active Bibliography; Co-citation; Clustered Documents; Version History; BibTeX @MISC{Chaganty_journalof, author = {Arun Tejasvi Chaganty and Percy Liang and C A. T. Chaganty and P. Liang and Chaganty Liang}, title = {Journal of Machine Learning Research 1–11 Supplementary Material for Spectral Experts for Estimating Mixtures of Linear Regressions}, year = {}} Share. Percy Liang This short note presents a new formal language, lambda dependency-based compositional semantics (lambda DCS) for representing logical forms in semantic parsing. Pranav Rajpurkar, Robin Jia, Percy Liang. CS221: Artificial Intelligence (Autumn 2012) ­ Percy Liang 37 Summary Linear models: prediction governed by Losscfunctions:ucapturecvarious desiderata (e.g., robustness) for both regression and binary classification (can be generalized to many other problems) Objective function: minimize loss over training data /Length 1337 OpenURL . Follow their code on GitHub. … Additionally, we procured a PDF copy of Artificial Intelligence: A Modern Approach by Stuart Russel … endobj [, Wed 10/31: Lecture 12: Generalization and approximation in Statistical Learning Theory (CS229T) Lecture Notes - percyliang/cs229t Approximation in Shallow NN. %���� Related. Percy Liang Associate Professor of Computer Science and Statistics (Courtesy) Dorsa Sadigh Assistant Professor of Computer Science and Electrical Engineering. Universality proof is loose: exponential number of units. [, Wed 11/07: Lecture 14: Online learning, online convex optimization, Follow the Leader (FTL) algorithm Designing and Interpreting Probes with Control Tasks. EM: Revisiting K-Means 53 1Reference: Percy Liang, CS221 (2015) • EM tries to maximize marginal likelihood • K-means • Is a special case of EM (for GMMs with variance tending to 0) • Objective: Estimate cluster centers • But don’t know which points belong to which clusters • Take an alternating optimization approach • Find the … Percy Liang’s Lecture Notes (Stanford) Martin Wainwright’s Lecture Notes (Berkeley) Additional References: 1.‘Learning with Kernels,’ B. Scholkopf and A. Smola, MIT Press, 2002. We scraped Piazza question, answers, tags, followups, and notes from the Autumn 2016 offering of CS 221 as well as the 2013 - 2016 offerings of CS 124, with the permission of Professors Percy Liang and Dan Jurafsky, respectively. [, Thu 11/01: Homework 2 (uniform convergence), Mon 11/05: Lecture 13: Restricted Approximability, overview of This preview shows page 1 - 3 out of 12 pages. Here are some areas I have worked on: Semantic parsing: Parse the input sentence into some representation of its meaning. Discriminative latent-variable models are typically learned using EM or gradient-based optimization, … I am interested in natural language processing. [, Mon 10/22: Lecture 9: VC dimension, covering techniques Notes. What is the advantage of deep networks? 1Reference: Percy Liang, CS221 (2015) 2Note: EM was first proposed in 1977. A number of useful references: Percy Liang's course notes from previous Upon completing this course, you will earn a Certificate of Achievement in Artificial Intelligence Principles and Techniques from the Stanford Center for Professional Development. �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���". If you have a spare hour and a half, I highly recommend you watch Percy Liang’s entire talk which this summary article was based on: Special thanks to Melissa Fabros for recommending Percy’s talk, Matthew Kleinsmith for highlighting the MIT Media Lab definition of “grounded” language, and Jeremy Howard and Rachel Thomas of fast.ai for faciliating our connection and conversation. Project: Predictable AI via Failure Detection and Robustness. Abstract. � �T ��f��Ej͏���8���H��8f�@��)���@���D���W�a�\ ��G@Nb���� ��P� In this … Assistant Professor of Computer Science and, by courtesy, of Statistics. endstream %PDF-1.5 Percy Liang on Learning Hidden Computational Processes Young Kun Ko on The Hardness of Sparse PCA [pdf] Tom Griffiths on Rationality, Heuristics, and the Cost of Computation [pdf] Decomposition of Errors. Amount Recommended: $255,160. pliang@cs.stanford.edu. /N 100 Percy Liang. The tables were randomly selected among Wikipedia tables with at least 8 rows and 5 columns. 2.‘Statistical Learning Theory,’ Vladimir N. Vapnik, Wiley, 1998. We are interested in calibration for structured prediction problems such as speech recognition, optical character recognition, and medical diagnosis. real analysis, Summary; Citations; Active Bibliography; Co-citation; Clustered Documents; Version History; BibTeX @MISC{Liang_learningdependency-based, author = {Percy Liang and Michael I. Jordan and Dan Klein}, title = {Learning Dependency-Based Compositional Semantics}, year = {}} Share. EMNLP 2019 (long papers). two-layer neural networks Existing datasets either focus exclusively on answerable questions, or use automatically generated … Percy Liang and Dan Klein (2007): Structured Bayesian Nonparametric Models with Variational Inference David Blei's group's topic modeling software (C, C++. The questions require multi-step reasoning and various data operations such as comparison, aggregation, and arithmetic computation. The dataset contains pairs table-question, and the respective answer. A Structual Probe for Finding Syntax in Word Representations. 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 10/10: Lecture 6: Rademacher complexity, margin theory stochastic setting Certificate. statistical learning theory course, Martin Wainwright's 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 John Hewitt and Percy Liang. Compositionality: requires exponential number of units in a shallow network. [, Mon 12/03: Lecture 19: Regret bound for UCB, Bayesian setup, Stanford University. Derivation for linear regression. [, Mon 11/12: Lecture 15: Follow the Regularized Leader (FTRL) algorithm 3.‘An Elementary Introduction to Statistical Learning Theory,’ Sanjeev Kulkarni and Gilbert Harman, Wiley, 2011. You may also earn a Professional Certificate in … Scribe: Percy Liang and David Malan Lecture 14: Ordered-file maintenance, analysis, order queries in lists, list labeling, external-memory model, cache-oblivious model Date: Monday, April 14, 2003 Scribe: Kunal Agrawal and Vladimir Kiriansky A few pointers: Our simple example came from this nice article by Percy Liang. Percy Liang Department of Computer Science Stanford University Stanford, CA 94305 Abstract In user-facing applications, displaying calibrated confidence measures— probabilities that correspond to true frequency—can be as important as obtaining high accuracy. Peter Bartlett's statistical learning theory course. According to media reports, a pair of hackers said on Saturday that the Firefox Web browser, commonly perceived as the safer and more customizable alternative to … offerings of this course, Peter Bartlett's statistical learning theory course, Boyd and [, Wed 11/14: Lecture 16: FTRL in concrete problems: online regression & expert problem, convex to linear reduction View Notes - 7-mdp1 from CS 221 at Stanford University. Abstract Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. OpenURL … linear algebra, x���o�6���+t��Z��.CV��=�;02c���#M�חI�q�6Z���N�h�����%-#�y��6��5d�)��D��H�qq�SL�"��. In order for AI to be safely deployed, the desired behavior of the AI system needs to be based on well-understood, realistic, and empirically testable assumptions. Percy Liang Lots of high-dimensional data... face images Zambian President Levy Mwanawasa has won a second term in o ce in an election his challenger Michael Sata accused him of rigging, o cial results showed on Monday. When Percy Liang isn't creating algorithms, he's creating musical rhythms. [, Mon 10/08: Lecture 5: Sub-Gaussian random variables, Rademacher complexity [Please refer to, Mon 10/29: Lecture 11: Total variation distance, Wasserstein distance, Wasserstein GANs Sham Kakade's statistical learning theory course. Boyd and Vandenberghe's Convex Optimization. (pdf) (bib) (blog) (code) (codalab) (slides) (talk). We then cleaned this data, by removing errant HTML and LaTeX symbols. Better basis? , 1998 previous offerings of this course shows page 1 - 3 out of 12 pages among Wikipedia with! 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