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MLDG

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

4/26

Locality-Sensitive Hashing

Kernelized Locality-Sensitive Hashing

Anshu

4/12

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

Causal 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

11/16

Submodularity

Learning Mixtures of Submodular Shells with Application to Document Summarization

Ruben & Karthik

11/9

Generative Models

Exploiting compositionality to explore a large space of model structures

Jason

10/19

Generative Models

Revisiting k-means: New Algorithms via Bayesian Nonparametrics

Karthik

10/12

Generative Models

An Innite Latent Attribute Model for Network Data

Ruben

9/28

Generative Models

Sparse Additive Generative Models of Text

Adith

9/14

Time Series Analysis

Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping

Ashesh

9/7

Statistical Estimators

Bag of Little Bootstraps

Karthik

4/6

Machine Learning and Game Theory

Machine Learning Markets

Karthik

11/2

Deep Learning

Parsing Natural Scenes and Natural Language with Recursive Neural Networks

Abhishek & Ainur

10/19

Graphical Models

Spectral Algorithm for Latent Tree Graphical Models

Karthik

10/5

Trading Representability for Scalability: Adaptive Multi-Hyperplane Machine for Nonlinear Classification

Nikos

9/28

Submodularity

Submodularity tutorial

Ashwin

9/21

Graphical Models

Minimum Probability Flow Learning

Nikos

9/14

Submodularity

Submodular meets Spectral

Karthik

9/7

Deep-Learning, Graphical Models

Sum-Product Networks: A New Deep Architecture

Karthik

4/29, 5/6, 5/13

Variational Methods

Tutorial on Variational Approximation Methods

Nikos

4/22

Deep Learning

Deep Boltzman Machines

Ainur

4/15

Deep Learning

Multimodal Deep Learning

Akram

4/8

Deep Learning

Fast Learning Alg. for Deep Belief Nets

Akram

4/1

Semi-Supervised Learning

Optimal Reverse Prediction

Nikos

3/11

Game Theory and Learning

Game-Theoretic Approach to Apprenticeship Learning

Ruben

3/4

Game Theory and Learning

Game Theory, On-line Prediction and Boosting

Karthik

2/25

Multi-Task Learning

Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity

Bishan

11/12

Vision

A Neuromorphic Approach to Computer Vision

Jason

11/5

Metric Learning

Metric Learning to Rank

Karthik

10/29

Clustering

Mining Clustering Dimensions

Ruben