Machine Learning Lunch

	Nov 16, 2009 First-Order Mixed Integer Linear Programming Sue Ann Hong
	Nov 2, 2009 Logic, probability, and human learning Prof. Charles Kemp
	Oct 26, 2009 Set estimation for statistical inference in brain imaging and active sensing Prof. Aarti Singh
	Oct 19, 2009 Applied machine learning in human-computer interaction research Moira Burke
	Oct 12, 2009 NSH 3305 Bayesian Browsing Model from Petabyte-scale Data Fan Guo
	Oct 12, 2009 NSH 3305 On the Local Optimality of LambdaRank Pinar Donmez
	Oct 5, 2009 Graph-Guided Fused Lasso for Sparse Structured-Output Regression Seyoung Kim
	Oct 5, 2009 RTG: A Recursive Realistic Graph Generator using Random Typing Leman Akoglu
	Sept 28, 2009 Towards a Principled Theory of Clustering Reza Bosagh Zadeh
	Sept 21, 2009 NSH 1507ICML 2009 Conference Review Session Blockwise Coordinate Descent Procedures for the Multi-task Lasso, with Applications to Neural Semantic Basis Discovery Mark Palatucci
	Sept 21, 2009 NSH 1507ICML 2009 Conference Review Session Hilbert space embeddings of conditional distributions with applications to dynamical systems Le Song
	May 4, 2009 Wean 4623 Click Chain Model in Web Search Fan Guo
	Apr 20, 2009 A Scalable Hierarchical Distributed Language Model Andriy Mnih
	Apr 13, 2009 Discourse Structure from Topic Models in Text and Video Jacob Eisenstein
	Apr 6, 2009 Fourier Theoretic Probabilistic Inference over Permutations Jonathan Huang
	Mar 30, 2009 Uncovering, understanding, and predicting links Jonathan Chang
	Mar 23, 2009Wean 4615A Painless function space embeddings of distributions: theory and applications Arthur Gretton
	Mar 20, 2009Friday, 3pm. Wean 4623. Snacks provided! Use of Hash in Machine Learning John Langford
	Mar 2, 2009 Machine learning approaches for understanding the genetic basis of complex traits Su-In Lee
	Feb 23, 2009 Nonnegative Matrix Factorization for Clustering and Combinatorial Optimizations Chris Ding
	Feb 16, 2009 Large Scale Scene Matching for Graphics and Vision James Hays
	Jan 26, 2009 Deep componential models for human motion Graham Taylor
	Dec 1, 2008 3-D Point Cloud Classification with Max-Margin Markov Networks Daniel Munoz
	Nov 24, 2008 Some Challenging Machine Learning Problems in Computational Biology: Time-Varying Networks Inference and Sparse Structured Input-Out Learning Eric Xing
	Nov 17, 2008 Rare Category Detection for Spatial Data Jingrui He
	Nov 10, 2008 Differentiable Sparse Coding David Bradley
	Nov 10, 2008 Partially Observed Maximum Entropy Discrimination Markov Networks Jun Zhu
	Nov 3, 2008 Inference Complexity as Learning Bias Pedro Domingos
	Oct 27, 2008 Local Minima Free Parameterized Appearance Models Minh Hoai Nguyen
	Oct 27, 2008 Object Recognition and Segmentation by Association Tomasz Malisiewicz
	Oct 20, 2008 Probabilistic Decision-Making Under Model Uncertainty Joelle Pineau
	Oct 13, 2008 Wean 4623 Activized Learning: Transforming Passive to Active with Improved Label Complexity Steve Hanneke
	Oct 6, 2008 Wean 4623KDD 2008 Conference Review Session Weighted Graphs and Disconnected Components: Patterns and a Generator Mary McGlohon
	Oct 6, 2008 Wean 4623KDD 2008 Conference Review Session Efficient Parallel Learning of Linear Dynamical Systems on SMPs Lei Li
	Sept 29, 2008 UAI/ACL 2008 Conference Review Session Feature Selection via Block-Regularized Regression Seyoung Kim
	Sept 29, 2008 UAI/ACL 2008 Conference Review Session Exploiting document structure and feature hierarchy for semi-supervised domain adaptation Andrew Arnold
	Sept 22, 2008 Kernelized Sorting Le Song
	May 5, 2008 Learning Patterns of the Brain: Machine Learning Challenges of fMRI Analysis Mark Palatucci
	Apr 28, 2008 mStruct: Inference of population structure in light of both genetic admixing and allele mutations Suyash Shringarpure
	Apr 28, 2008 Query-Specific Learning for Graphical Models Anton Chechetka
	Apr 14, 2008 Learning Driving Route Preferences Brian Ziebart
	Apr 7, 2008 Learning Stable Linear Dynamical Systems Sajid M. Siddiqi
	Mar 3, 2008 Probability Distributions on Permutations Jonathan Huang
	Feb 25, 2008 High Dimensional Sparse Regression and Structure Estimation Shuheng Zhou
	Feb 18, 2008 Discovering Cyclic Causal Models by Independent Components Analysis Gustavo Lacerda
	Feb 11, 2008 Overview of New Developments in Boosting Joseph Bradley
	Feb 4, 2008 Relational Learning as Collective Matrix Factorization Ajit Singh
	Jan 21, 2008 Structured Prediction: Maximum Margin Techniques Nathan Ratliff
	Dec 10, 2007 Maximum Likelihood Estimation in Latent Class Models for Contingency Table Data Yi Zhou
	Dec 3, 2007 Causal discovery based on non-gaussianity Patrick Hoyer
	Nov 26, 2007 Statistical Parsing Triptych: Jeopardy, Morphosyntax, and M-Estimation Noah Smith
	Nov 19, 2007 The Maximum Entropy Principle Miroslav Dudik
	Nov 12, 2007 Spatiotemporal Stochastic Processes and Their Prediction Cosma Shalizi
	Nov 05, 2007 Stochastic Processes and their Prediction Cosma Shalizi
	Oct 29, 2007 Proximity on Graphs: Definitions, Fast Solutions and Applications Hanghang Tong
	Oct 22, 2007 Machine Learning in in vivo CNS Drug Discovery Jeff Schneider
	Oct 16, 2007 (Tue) Visualizing Social Media: Principles and Techniques Matthew Hurst
	Oct 1, 2007 Random Walks on Graphs: A General Overview Purnamrita Sarkar
	Sept 24, 2007 Some Topics in Spam Filtering D. Sculley
	May 7, 2007 Probabilistic Inference in Distributed Systems Stanislav Funiak
	Apr 23, 2007 Learning without the loss function John Langford
	Apr 16, 2007 Sparsity recovery and structure learning Pradeep Ravikumar
	April 2, 2007 A unifying view of component analysis (from a computer vision perspective) Fernando De la Torre
	Mar 19, 2007 Active Learning of Binary Classifiers Nina Balcan
	Mar 5, 2007 Features, kernels, and similarity functions Avrim Blum
	Feb 26, 2007 Models of real-world networks (Part II) Jure Leskovec
	Feb 19, 2007 The structure and function of real-world graphs and networks (Part I) Jure Leskovec
	Feb 12, 2007 Discrete Markov Random Fields -- the Inference story Pradeep Ravikumar
	Jan 22, 2007 NIPS 2006 Conference Review Session.
	Jan 22, 2007 Greedy Layer-Wise Training of Deep Networks. Nathan Ratliff
	Jan 22, 2007 Approximate inference using planar graph decomposition. Pradeep Ravikumar

Past talks

• The 2006 ML talks.
• The 2005 ML talks.
• The 2004 ML talks.
• The 2003 ML talks.
• The 2002 ML talks.
• The 2001 ML talks.
• The 2000 ML talks.
• The 1999 ML talks.
• Older talks (1992-93, 1993-94, 1994-95, 1995-96, 1996-97, 1997-98)

Related Seminars

• Machine Learning Seminar Series
• Intelligence Seminar Series
• Statistics Seminars
• Bioinformatics
• School of Computer Science (SCS) Seminars
• Robotics Institute (RI)Seminars
• Center for the Neural Basis of Cognition (CNBC) Seminars
• VASC Seminar Series
• Language Technologies Institute (LTI) Sponsored Seminars
• Human-Computer Interaction Institute (HCII) Seminar Series

Conferences

Lists of conferences

David Aha's ML/CBR Conference Announcements
Neural Network, Vision, And Speech Conferences
IEEE Conference Database

ICML -- International Conference on Machine Learning

KDD -- International Conference on Knowledge Discovery and Data Mining

COLT -- Conference on Computational Learning Theory

NIPS -- Neural Information Processing Systems

AAAI -- National Conference on Artificial Intelligence

IJCAI -- International Joint Conference on Artificial Intelligence

UAI -- Conference on Uncertainty in Artificial Intelligence

ILP -- International Conference on Inductive Logic Programming

Resources

General Machine Learning Resources

• David Aha's List of Machine Learning Resources
• ÖFAI Machine Learning Resources
• The UCI Repository
• Machine Learning Department, CMU 
• Machine Learning Journal
• SGI's MLC++   (a library of machine learning code)
• CMU Learning Lab
• Home pages for people in Machine Learning
• CMU StatLib
• MachineLearning.com
• LearningTheory.org
• Tom Minka:  Statistical Learning / Pattern Recognition Glossary and Papers and Tutorial Notes
• Sam Roweis:  Tutorial Notes

Reinforcement Learning Resources

• RL papers by CMU people (including a handy survey of RL)
• The excellent Reinforcement Learning Repository at University of Massachusetts, Amherst
• Other RL repositories: Rich Sutton | Neuroprose
• Workshop info: Spring 1996 NSF Workshop on Reinforcement Learning

Support Vector Machine Resources

• Kernel-Machines.org with pointers to Tutorials
• Support Vector Machines the book
• Learning with Kernels the book - support vector machines, regularization, optimization and beyond

Robot Learning Resources

• CMU Robot Learning Lab
• CMU Coral Research Group
• Workshop papers: ML94 Robot Learning

Related Sites

• Cambridge Control Engineering
• On-line CS Techreports