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This Spring we will meet every Friday@4:00pm in 5126 Upson344 Gates Hall (Breakout room).

Papers Read

Date	Presenter	Topic(s)	Resources/Papers	Other activities/ comments
9/6/2013	Ruben	General ML	A Few Useful Things to Know about Machine Learning
9/20/2013	Karthik	Active Learning, Crowdsourcing	Tutorial style discussion. Focus on Pairwise Ranking Aggregation in a Crowdsourced Setting	Paul Bennett (AI Seminar)
9/27/2013	Ashwin	Method of moments	A bit of background from 1) http://en.wikipedia.org/wiki/Method_of_moments_(statistics) 2) Chapter 7 of the following book. Followed by freeform discussion on http://newport.eecs.uci.edu/anandkumar/pubs/AnandkumarEtal_mixtures12.pdf
10/4/2013	Adith	Distributed Representations	Freeform discussion. The Parallel Distributed Processing Approach to Semantic Cognition
10/18/2013	Hema	Vision	-
10/25/2013	Chenhao	Practice Talk	-
11/1/2013	Ashesh	Human-In-Loop Learning	Fine-Grained Crowd sourcing for Fine-Grained Recognition
11/15/2013	Stefano
11/22/2013	Yin

Date	Topic	Paper	Discussion Leader
7/18	Inverse Reinforcement Learning	Tutorial	Ashesh
7/11	Bayesian Nonparametrics	Dirichlet processes, its variants and applications	Yun
6/27	Deep Learning	Deep Learning (Examples, Thoughts and Ideas)	Moontae
6/13	Bioinformatics	Tutorial on Machine Learning problems in Bioinformatics and Genetics	Brad
6/6	Structured Learning	A Structural SVM Based Approach for Optimizing Partial AUC	Ruben
5/23	Deep Learning	Tutorial on Deep Learning	Ian

Date	Topic	Paper	Discussion Leader
4/26	Locality-Sensitive Hashing	Kernelized Locality-Sensitive Hashing	Anshu
4/12 3/29	Metric Learning	A Geometric Take on Metric Learning	Ozan
4/5	Metric Learning	Robust Structural Metric Learning	Karthik
3/15	Submodularity	An Online Algorithm for Maximizing Submodular Functions	Karthik
3/08	Large-Scale Learning	Random Features for Large-Scale Kernel Machines	Anshumali
3/01	Large-Scale Learning	Scaling Up Coordinate Descent Algorithms for Large L_1 Regularization Problems	Ashesh
2/22	Metric Causal Learning A Geometric Take on Metric Learning	On causal and anticausal learning	Chenhao
2/15	Submodularity	Algorithms for Approximate Minimization of the Difference	Ruben
2/8	Large-Scale Learning	Block Splitting for Large-Scale Distributed Learning	Moontae

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