'Sample complexity' presentation slideshows

New Theoretical Frameworks for Machine Learning

New Theoretical Frameworks for Machine Learning. Maria-Florina Balcan. Thesis Proposal. 05/15/2007. Thanks to My Committee. Avrim Blum. Manuel Blum. Tom Mitchell. Yishay Mansour. Santosh Vempala. The Goal of the Thesis. New Theoretical Frameworks for Modern Machine Learning Paradigms.

690 views • 54 slides

CS 391L: Machine Learning: Computational Learning Theory

CS 391L: Machine Learning: Computational Learning Theory. Raymond J. Mooney University of Texas at Austin. Learning Theory.

794 views • 53 slides

Attribute-Efficient Learning of Monomials over Highly-Correlated Variables

Attribute-Efficient Learning of Monomials over Highly-Correlated Variables. Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli Columbia University Yahoo Research, Aug. 2019. General Learning Problem. Given: , drawn i.i.d.

511 views • 51 slides

Joseph K. Bradley

Sample Complexity of CRF Parameter Learning. Joseph K. Bradley. Joint work with Carlos Guestrin CMU Machine Learning Lunch talk on work appearing in AISTATS 2012. 4 / 9 / 2012. Markov Random Fields (MRFs). Goal: Model distribution P(X) over random variables X. E.g.,.

624 views • 50 slides

Private Statistics: A TCS Perspective

Private Statistics: A TCS Perspective. Gautam Kamath Simons Institute University of Waterloo Data Privacy: Foundations and Applications Boot Camp January 29, 2019. Outline. Setting and Goals Hypothesis Testing Distribution Estimation (Some) Other Statistical Tasks.

462 views • 43 slides

Required Sample size for Bayesian network Structure learning

Required Sample size for Bayesian network Structure learning. Samee Ullah Khan and Kwan Wai Bong Peter. Outline. Motivation Introduction Sample Complexity Sanjoy Dasgupta Russell Greiner Nir Friedman David Haussler Summary Conclusion. Motivation.

511 views • 42 slides

CS 391L: Machine Learning: Computational Learning Theory

CS 391L: Machine Learning: Computational Learning Theory. Raymond J. Mooney University of Texas at Austin. Learning Theory.

449 views • 39 slides

Friday, 25 January 2008 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 02 of 42. Representation Bias vs. Search Bias and Intro to Decision Trees. Friday, 25 January 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Readings: Chapter 2, Mitchell

426 views • 32 slides

New Horizons in Machine Learning

New Horizons in Machine Learning. Avrim Blum CMU. This is mostly a survey, but portions near the end are joint work with Nina Balcan and Santosh Vempala. [Workshop on New Horizons in Computing, Kyoto 2005]. What is Machine Learning?.

433 views • 31 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Carla P. Gomes gomes@cs.cornell.edu Module: Computational Learning Theory: PAC Learning (Reading: Chapter 18.5). Computational learning theory. Inductive learning:

374 views • 28 slides

Chapter 11: Analytical Learning

Chapter 11: Analytical Learning. Inductive learning training examples Analytical learning prior knowledge + deductive reasoning Explanation based learning - prior knowledge : analyze, explain how each training

2.03k views • 26 slides

Machine Learning Chapter 7. Computational Learning Theory

Machine Learning Chapter 7. Computational Learning Theory. Tom M. Mitchell. Computational Learning Theory (1/2). Computational learning theory Setting 1: learner poses queries to teacher Setting 2: teacher chooses examples Setting 3: randomly generated instances, labeled by teacher

451 views • 25 slides

Attribute-Efficient Learning of Monomials over Highly-Correlated Variables

Attribute-Efficient Learning of Monomials over Highly-Correlated Variables. Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli Columbia University Algorithmic Learning Theory 2019. Learning Sparse Monomials. A Simple Nonlinear Function Class. 3 dimensions. In dimensions. Ex:.

250 views • 24 slides

PeptideProphet Explained

PeptideProphet Explained. Brian C. Searle Proteome Software Inc. www.proteomesoftware.com 1336 SW Bertha Blvd, Portland OR 97219 (503) 244-6027

341 views • 22 slides

Other Concept Classes

Other Concept Classes. k -term DNF: Disjunctions of at most k unbounded conjunctive terms: ln(| H |)=O( kn ) k -DNF: Disjunctions of any number of terms each limited to at most k literals: ln(| H |)=O( n k ) k -clause CNF: Conjunctions of at most k unbounded disjunctive clauses:

227 views • 22 slides

PEGASOS Primal Efficient sub-GrAdient SOlver for SVM

478 views • 19 slides

Introduction

The sample complexity of learning Bayesian Networks Or Zuk*^, Shiri Margel* and Eytan Domany* *Dept . of Physics of Complex Systems Weizmann Inst. of Science ^Broad Inst. Of MIT and Harvard. Introduction. X 2. X 1. 0. 1. 0. 0.95. 0.05. 1. 0.2. 0.8.

224 views • 16 slides

Learning Voting Trees

Learning Voting Trees. Ariel D. Procaccia, Aviv Zohar, Yoni Peleg, Jeffrey S. Rosenschein. Lecture Outline. Voting. PAC. Results. Limitations. Voting and Voting Trees PAC learning Results Limitations. Voting . Voting. PAC. Results. Limitations.

286 views • 13 slides

Machine Learning: Lecture 8

Machine Learning: Lecture 8. Computational Learning Theory (Based on Chapter 7 of Mitchell T.., Machine Learning, 1997). Overview. Are there general laws that govern learning?

691 views • 12 slides

Sample complexity for Multiresolution ICA

Sample complexity for Multiresolution ICA. Doru Balcan Nina Balcan Avrim Blum. Introduction. Adaptive image representations Efficient encoding / Compression

195 views • 7 slides