As these talks are intended as overviews, time permitting, I plan to give two mini-tutorials: The first will be on the idea of counterfactuals/potential outcomes. Basically answering the question: how does data obtained from a simple randomized experiment differ from that obtained from an observational study, and how does that weaken inferences that can be obtained. The second will require assume a little bit of background on Bayesian networks and will answer the question: if we obtain data from a subset of the variables in a causal Bayesian network, which causal effects are identified and how can they be computed efficiently.

**Title:**Causality

**Speaker:**Thomas Richardon, University of Washington

**When:**Tuesday, February 28, 2012 - 12:30

**Location:**EEB 037

**Title:**Counting and Sampling Solutions of Combinatorial Problems

**Speaker:**Ashish Sabharwal, IBM T,J, Watson

**When:**Tuesday, February 21, 2012 - 12:30

**Location:**EEB 037

This tutorial presents a survey of algorithms that are used for counting and sampling of SAT problems.

**Title:**Knowledge Extraction for Biomedical Text

**Speaker:**Hoifung Poon, Microsoft Research

**When:**Tuesday, February 14, 2012 - 12:30

**Location:**

This tutorial summarizes the literature on knowledge extraction in scientific domains, such as biomedical texts.

**Title:**Machine Learning for Information Retrieval

**Speaker:**Niranjan Balasubramanian, University of Washington

**When:**Tuesday, February 7, 2012 - 12:30

**Location:**EEB 037

This tutorial discusses the machine learning techniques popular in the information retrieval subcommunity such as ranking techniques, pagerank, etc.

**Title:**Trajectory Optimization with Differential Dynamic Programming

**Speaker:**Tom Erez, University of Washington

**When:**Tuesday, January 31, 2012 - 12:30

**Location:**EEB 037

This tutorial gives an introduction to the control theory, in particular, discussing the trajectory optimization techniques.

**Title:**Latent Variable Models of Lexical Semantics

**Speaker:**Alan Ritter, University of Washington

**When:**Tuesday, January 24, 2012 - 12:30

**Location:**EEB 037

This tutorial discusses probabilistic models popular in the NLP lexical semantics community.

**Title:**Latent Factor Models for Relational and Network Data

**Speaker:**Peter Hoff, University of Washington

**When:**Tuesday, January 17, 2012 - 12:30

**Location:**EEB 037

This tutorial discusses probabilistic models for social and other network data.

**Title:**Submodular Functions and Active Learning

**Speaker:**Andrew Guillory, University of Washington

**When:**Tuesday, January 10, 2012 - 12:30

**Location:**EEB 037

This tutorial presents a brief survey of active learning, submodular functions, and the interesting algorithms and analyses at their intersection. Minimal background knowledge is assumed, and emphasis is placed on open problems and gaps between theory and practice. Slides at: http://ml.cs.washington.edu/www/media/presentations/submodularity_tutori...

**Title:**Machine Learning and Big Data Analysis

**Speaker:**Alice Zheng

**When:**Tuesday, November 29, 2011 - 12:30

**Location:**CSE 305

Our world is becoming more data driven. With the spread of ubiquitous sensors, network connectivity, and massive storage capabilities, we are able to collect more and more data. But our computation and analysis capabilities have not increased at a comparable rate. Computer scientists are facing looming questions such as "How do we deal with the massive amounts of data we are collecting? How can we extract value out of data?" A sub-question relevant to machine learning researchers is "what role will machine learning and data mining play?" Through a survey of current sources of Big Data and analysis workflow patterns, this talk aims to shed light on the latter question.

**Title:**Entire Relaxation Path for Maximum Entropy Models

**Speaker:**Yoram Singer

**When:**Tuesday, November 22, 2011 - 12:30

**Location:**CSE 305

We describe a relaxed and generalized notion of maximum entropy problems for multinomial distributions. By introducing a simple re-parametrization we are able to derive an efficient homotopy tracking scheme for the entire relaxation path using linear space and quadratic time. We also show that the Legendre dual of the relaxed maximum entropy problem is the task of finding the maximum likelihood estimator for an exponential distribution with L1 regularization. Hence, our solution can be used for problems such as language modeling with sparse parameter representation.