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

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
Reinforcement Learning Resources
Support Vector Machine Resources
Robot Learning Resources
Related Sites
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