Uploaded By sttg6. Sham Kakade's statistical learning theory course. linear algebra, There is no required text for the course. 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. Approximation in Shallow NN. 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. Notes. Amount Recommended: $255,160. Percy Liang This short note presents a new formal language, lambda dependency-based compositional semantics (lambda DCS) for representing logical forms in semantic parsing. (pdf) (bib) (blog) (code) (codalab) (slides) (talk). In this … problems, error decomposition [, Wed 09/26: Lecture 2: asymptotics of maximum likelihood estimators (MLE) [, Mon 10/01: Lecture 3: uniform convergence overview, finite 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 Universality of NN. 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 How does it improve bound for various classes of functions? endstream /Type /ObjStm hypothesis class [, Wed 10/03: Lecture 4: naive epsilon-cover argument, concentration inequalities From notes of Percy Liang. Spectral methods for learning latentvariable models (joint work with Daniel Hsu, Sham Kakade, Arun … [, Thu 11/01: Homework 2 (uniform convergence), Mon 11/05: Lecture 13: Restricted Approximability, overview of /N 100 Better basis? EMNLP 2019 (long papers). A Structual Probe for Finding Syntax in Word Representations. Fp(t�� ��%4@@G���q�\ %PDF-1.5 We are interested in calibration for structured prediction problems such as speech recognition, optical character recognition, and medical diagnosis. Liang, who went to high school in Arizona, has been playing piano since the age of eight and won … [, Mon 10/22: Lecture 9: VC dimension, covering techniques [. 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. Additionally, we procured a PDF copy of Artiﬁcial Intelligence: A Modern Approach by Stuart Russel … [, Wed 10/24: Lecture 10: Covering techniques, overview of GANs Vandenberghe's Convex Optimization, Sham Kakade's /Filter /FlateDecode [, Wed 10/17: Lecture 8: Margin-based generalization error of Project Summary. [, Wed 11/28: Lecture 18: Multi-armed bandit problem in the NAACL 2019 (short … The questions require multi-step reasoning and various data operations such as comparison, aggregation, and arithmetic computation. You may also earn a Professional Certificate in … %���� /Filter /FlateDecode 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. Peter Bartlett's statistical learning theory course. To scale up inﬂuence functions to modern machine learning … OpenURL … and, Machine learning (CS229) or statistics (STATS315A), Convex optimization (EE364A) is recommended, Mon 09/24: Lecture 1: overview, formulation of prediction Stanford University. stream Runner up best paper. … Percy Liang Associate Professor of Computer Science and Statistics (courtesy) … [, Wed 11/07: Lecture 14: Online learning, online convex optimization, Follow the Leader (FTL) algorithm What is the advantage of deep networks? 2 0 obj << two-layer neural networks [Please refer to, Mon 10/29: Lecture 11: Total variation distance, Wasserstein distance, Wasserstein GANs Pages 12. Boyd and Vandenberghe's Convex Optimization. �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�����/���� In particular, I am interested in executable representations such as database queries or … We then cleaned this data, by removing errant HTML and LaTeX symbols. online learning offerings of this course, Peter Bartlett's statistical learning theory course, Boyd and View Notes - 7-mdp1 from CS 221 at Stanford University. This preview shows page 1 - 3 out of 12 pages. 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. Abstract. The tables were randomly selected among Wikipedia tables with at least 8 rows and 5 columns. Follow their code on GitHub. [, Mon 10/08: Lecture 5: Sub-Gaussian random variables, Rademacher complexity Percy Liang and Dan Klein (2007): Structured Bayesian Nonparametric Models with Variational Inference David Blei's group's topic modeling software (C, C++. 2.‘Statistical Learning Theory,’ Vladimir N. Vapnik, Wiley, 1998. Assistant Professor of Computer Science and, by courtesy, of Statistics. 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. I am interested in natural language processing. Certificate. 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 : … A few pointers: Our simple example came from this nice article by Percy Liang. probability theory, OpenURL . Existing datasets either focus exclusively on answerable questions, or use automatically generated … Percy Liang Associate Professor of Computer Science and Statistics (Courtesy) Dorsa Sadigh Assistant Professor of Computer Science and Electrical Engineering. [, Wed 12/05: Lecture 20: Information theory, regret bound for �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����`�$~���o]N���". Designing and Interpreting Probes with Control Tasks. Percy Liang Lots of high-dimensional data... face images Zambian President Levy Mwanawasa has won a second term in o ce in an election his challenger Michael Sata accused him of rigging, o cial results showed on Monday. Better bound? [, Mon 10/15: Lecture 7: Rademacher complexity, neural networks … Deep vs. … percyliang has 12 repositories available. Abstract. [, Mon 11/12: Lecture 15: Follow the Regularized Leader (FTRL) algorithm [, Wed 10/10: Lecture 6: Rademacher complexity, margin theory Compositional question answering begins by mapping questions to logical forms, but training a … [, Wed 11/14: Lecture 16: FTRL in concrete problems: online regression & expert problem, convex to linear reduction K�i���,% `) �Ԑ̀dR�i��t�o �l�Rl�M$Z�Ѱ��$1�)֔hXG���e*5�I��'�I��Rf2Gradgo"�4���h@E #- R x�-<>�)+��3e�M��t�`� pliang@cs.stanford.edu. real analysis, >> Here are some areas I have worked on: Semantic parsing: Parse the input sentence into some representation of its meaning. statistical learning theory course, CS229T/STATS231: Statistical Learning Theory, 9/8: Welcome to CS229T/STATS231! John Hewitt and Christopher D. Manning. 1Reference: Percy Liang, CS221 (2015) 2Note: EM was first proposed in 1977. By eliminating variables and making existential quantification implicit, lambda DCS logical forms are generally more compact than those in lambda calculus. stream Compositionality: requires exponential number of units in a shallow network. Summary; Citations; Active Bibliography; Co-citation; Clustered Documents; Version History; BibTeX @MISC{Liang_learningdependency-based, author = {Percy Liang and Michael I. Jordan and Dan Klein}, title = {Learning Dependency-Based Compositional Semantics}, year = {}} Share. Percy Liang Department of Computer Science Stanford University Stanford, CA 94305 Abstract In user-facing applications, displaying calibrated conﬁdence measures— probabilities that correspond to true frequency—can be as important as obtaining high accuracy. Thompson Sampling endobj [, Mon 11/26: Lecture 17: Multi-armed bandit problem, general OCO with partial observation 378 0 obj << Statistical Learning Theory (CS229T) Lecture Notes - percyliang/cs229t The dataset contains pairs table-question, and the respective answer. Upon completing this course, you will earn a Certificate of Achievement in Artificial Intelligence Principles and Techniques from the Stanford Center for Professional Development. Universality proof is loose: exponential number of units. 3.‘An Elementary Introduction to Statistical Learning Theory,’ Sanjeev Kulkarni and Gilbert Harman, Wiley, 2011. xڥW�r�6}�W�����$;�t\7�N�c��_ �0�������H'�cStg, g���]��"�IEdH�(1$""#�HĚ�RI"!��HI� Wassersetin GANs Moreover, users of- ten ask questions that diverge from the model’s training data, making errors more likely and thus abstention more critical. /Length 1337 Discriminative latent-variable models are typically learned using EM or gradient-based optimization, … John Hewitt and Percy Liang. Scribe: Percy Liang and David Malan Lecture 14: Ordered-file maintenance, analysis, order queries in lists, list labeling, external-memory model, cache-oblivious model Date: Monday, April 14, 2003 Scribe: Kunal Agrawal and Vladimir Kiriansky Project: Predictable AI via Failure Detection and Robustness. Abstract Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. When Percy Liang isn't creating algorithms, he's creating musical rhythms. Pang Wei Koh 1Percy Liang Abstract How can we explain the predictions of a black-box model? x���o�6���+t��Z��.CV��=�;02c���#M�חI�q�6Z���N�h�����%-#�y��6��5d�)��D��H�qq�SL�"��. Percy Liang. � �T ��f��Ej͏���8���H��8f�@��)���@���D���W�a�\ ��G@Nb���� ��P� Percy Liang's course notes from previous offerings of this course. >> Related. EM: Revisiting K-Means 53 1Reference: Percy Liang, CS221 (2015) • EM tries to maximize marginal likelihood • K-means • Is a special case of EM (for GMMs with variance tending to 0) • Objective: Estimate cluster centers • But don’t know which points belong to which clusters • Take an alternating optimization approach • Find the … Thompson sampling [, Wed 10/31: Lecture 12: Generalization and approximation in #�;���$���J�Y����n"@����)|��Ϝ�L�?��!�H�&� ��D����@ %BHa�`�Ef�I�S��E�� �T 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 … 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. statistical learning theory course, Martin Wainwright's A number of useful references: Percy Liang's course notes from previous Decomposition of Errors. 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. Martin Wainwright's statistical learning theory course 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 Derivation for linear regression. 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. [, Mon 12/03: Lecture 19: Regret bound for UCB, Bayesian setup, 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: 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. OpenURL . stochastic setting 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] Pranav Rajpurkar, Robin Jia, Percy Liang. /First 813 Randomly selected among Wikipedia tables with at least 8 rows and 5 columns worked on Semantic... 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