## Tuesday, May 3, 2016 - 12:00

## Supersizing Self-Supervision: ConvNets and Common sense without manual supervision

**Speaker:**Abhinav Gupta (CMU)

**Location:**CSE 305

## Tuesday, April 19, 2016 - 12:00

## Deep Robotic Learning

**Speaker:**Sergey Levine

**Location:**CSE 305

## Friday, April 1, 2016 - 12:00

## Machine learning approach to identify novel cancer therapeutic targets

**Speaker:**Su-In Lee

**Location:**CSE 305

## Tuesday, March 1, 2016 - 12:00

## Statistical Methods for Differential Network Analysis

**Speaker:**Ali Shojaie (UW)

**Location:**CSE 305

## Tuesday, February 16, 2016 - 12:00

## Deep Neural Network for Fast Object Detection and Newtonian Image Understanding

**Speaker:**Mohammad Rastegari (AI2)

**Location:**CSE 305

## Tuesday, February 2, 2016 - 12:00

## Structured Learning Algorithms for Entity Linking and Semantic Parsing

**Speaker:**Ming-Wei Chang, Microsoft Research

**Location:**CSE 305

## Tuesday, January 12, 2016 - 12:00

## Discovering Hidden Structure in the Sparse Regime

**Speaker:**Sham Kakade

**Location:**Gates Commons

## Tuesday, June 2, 2015 - 12:30

## Statistical machine learning methods for the analysis of large networks

**Speaker:**Edo Airoldi

**Location:**CSE 305

Edo Airoldi received a PhD from Carnegie Mellon University in 2007, working at the intersection of statistical machine learning and computational social science with Stephen Fienberg and Kathleen Carley. His PhD thesis explored modeling approaches and inference strategies for analyzing social and biological networks. Until December 2008, he was a postdoctoral fellow in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University working with Olga Troyanskaya and David Botstein. They developed mechanistic models of regulation, leveraging of high-thoughput technology, to gain insights into aspects of cellular dynamics that are not directly measurable at the desired resolution, such as growth rate. He joined the Statistics Department at Harvard University in 2009.

## Tuesday, May 19, 2015 - 12:30

## Diverse Particle Selection for High-Dimensional Inference in Graphical Models

**Speaker:**Erik Sudderth, Brown University

**Location:**CSE 305

Rich graphical models for real-world scene understanding encode the shape and pose of objects via high-dimensional, continuous variables. We describe a particle-based max-product inference algorithm which maintains a diverse set of posterior mode hypotheses, and is robust to initialization. At each iteration, the set of particle hypotheses is augmented via stochastic proposals, and then reduced via an optimization algorithm that minimizes distortions in max-product messages. Our particle selection metric is submodular, and thus efficient greedy algorithms have rigorous optimality guarantees. By avoiding the stochastic resampling steps underlying standard particle filters, we also avoid common degeneracies where particles collapse onto a single hypothesis. Our approach significantly outperforms previous particle-based algorithms in the estimation of human pose from single images, and the prediction of protein side-chain conformations.

Erik B. Sudderth is an Assistant Professor in the Brown University Department of Computer Science. He received the Bachelor's degree (summa cum laude, 1999) in Electrical Engineering from the University of California, San Diego, and the Master's and Ph.D. degrees (2006) in EECS from the Massachusetts Institute of Technology. His research interests include probabilistic graphical models; nonparametric Bayesian methods; and applications of statistical machine learning in computer vision and the sciences. He received an NSF CAREER award, and was named one of "AI's 10 to Watch" by IEEE Intelligent Systems Magazine

## Wednesday, May 6, 2015 - 12:30

## Graphical Modeling with the Bethe Approximation

**Speaker:**Tony Jebara, Department of Computer Science, Columbia University

**Location:**Room CSE 305, Allen Center