About
The Machine Learning lunch is a weekly seminar which has the goal of bringing together the different people at CMU working on related fields to discuss their work. In the past a broad range of topics has been discussed: reinforcement learning, machine learning in general, statistical AI, statistical learning theory, robot learning, text learning, etc. The talks have always been enjoyable and have ranged from quite informal to formal conference style talks. It is also a great forum to practice conference talks, bounce around new ideas and for guests from other universities and industry to speak. Currently the talks are sponsored by //**MLD - the Machine Learning Department of the School of Computer Science.
The goal of MLD is slightly broader than that of these talks - it brings together the many departments working on similar topics at CMU. The series has been going on for quite a few years. In earlier days it was called the Reinforcement Learning Lunch because of the emphasis on reinforcement learning. As the topics broadened, the name was changed to the Machine Learning Lunch.
Organizing committee: Amr Ahmed, Polo Chau, Steve Hanneke, Sue Ann Hong, Nathan Ratliff
{{http://l.yimg.com/a/i/ww/beta/y3.gif}} This lecture series is being kindly sponsored by Yahoo! Academic Relations
Videos

Probabilistic Decision-Making Under Model Uncertainty
Jan 15, 2009
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12101 views

Weighted Graphs and Disconnected Components: Patterns and a Generator
Mar 29, 2009
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5396 views

Overview of New Developments in Boosting
Feb 21, 2008
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8623 views

Partially Observed Maximum Entropy Discrimination Markov Networks
Jan 15, 2009
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5644 views

Differentiable Sparse Coding
Jan 15, 2009
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6866 views

Local Minima Free Parameterized Appearance Models
Jan 15, 2009
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6857 views

Relational Learning as Collective Matrix Factorization
Feb 14, 2008
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10059 views

Rare Category Detection for Spatial Data
Jan 15, 2009
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8315 views

Structured Prediction: Maximum Margin Techniques
Feb 7, 2008
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7960 views

Some Challenging Machine Learning Problems in Computational Biology: Time-Varyin...
Jan 15, 2009
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7950 views

Feature Selection via Block-Regularized Regression
Oct 21, 2008
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5694 views
Efficient Parallel Learning of Linear Dynamical Systems on SMPs
Mar 29, 2009
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4527 views

Object Recognition and Segmentation by Association
Jan 15, 2009
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6263 views

Learning Patterns of the Brain: Machine Learning Challenges of fMRI Analysis
Oct 21, 2008
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8504 views

Large Scale Scene Matching for Graphics and Vision
Mar 29, 2009
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6259 views

Activized Learning: Transforming Passive to Active with Improved Label Complexit...
Jan 15, 2009
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6672 views

Probability Distributions on Permutations: Compact Representations and Inference
Apr 17, 2008
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9878 views

Exploiting document structure and feature hierarchy for semi-supervised domain a...
Oct 21, 2008
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5313 views

Discovering Cyclic Causal Models by Independent Components Analysis
Feb 27, 2008
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6275 views

Inference Complexity as Learning Bias
Jan 15, 2009
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4852 views