PDF-Tensor decompositions for learning latest variable models

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AnandkumarGeHsuKakadeandTelgarskyKeywordslatentvariablemodelstensordecompositionsmixturemodelstopicmodelsmethodofmomentspowermethod1IntroductionThemethodofmomentsisaclassicalparameterestima

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Tensor decompositions for learning latest variable models: Transcript

AnandkumarGeHsuKakadeandTelgarskyKeywordslatentvariablemodelstensordecompositionsmixturemodelstopicmodelsmethodofmomentspowermethod1IntroductionThemethodofmomentsisaclassicalparameterestima. IndexTopicarraytensor,1%*t%(tensor),1%t*%(tensor),1%t*t%(tensor),1aperm,2matmult,2tensor,14 CHAPTER 9 . DUMMY VARIABLE REGRESSION MODELS. Textbook: . Damodar. N. Gujarati (2004) . Basic Econometrics. , 4th edition, The McGraw-Hill Companies. The types of variables that we have encountered in the preceding chapters were essentially ratio scale.. The General Case. STA431: Spring 2013. See last slide for copyright information. An Extension of Multiple Regression. More than one regression-like equation. Includes latent variables. Variables can be explanatory in one equation and response in another. Presented by Zhou Yu. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. M.Pawan. Kumar Ben Packer Daphne . Koller. , Stanford University. 1. Aim: . Based on the work with. Masafumi. . Fukuma. . and . Sotaro. . Sugishita. . (Kyoto Univ.). Naoya. . Umeda. . (Kyoto Univ.). [arXiv:1503.08812. ][JHEP . 1507 (2015) 088] . “. Random volumes from matrices. Tensor Decomposition and Clustering. Moses . Charikar. Stanford University. 1. Rich theory of analysis of algorithms and complexity founded on worst case analysis. Too pessimistic. Gap between theory and practice. What is Science?. 1.1 notes. Science. is . a systematic . way of studying the world. It is a way of learning more about the natural world and its natural events and conditions. . Scientists want to know . L. earning Trust. Latest news. We held our second board meeting on the 15. th. November and have some exciting progress to share with you.. . A combined choir from all the five schools under the direction of . Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. HKUST. 2014. HKUST. 1988. Latent Tree Models. Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Rong Ge. Duke University. Joint work with Sanjeev Arora, . Tengyu. Ma, Andrej . Risteski. “Provable Learning of Noisy-OR Networks” . STOC 2017. arxiv:1612.08795. “New practical algorithms for learning Noisy-OR networks via symmetric NMF”. Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation.

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