AI & Statistics 2005


Invited Speakers



Craig Boutilier

"Regret-based Methods for Decision Making and Preference Elicitation "

Preference elicitation is generally required when making or recommending decisions on behalf of users whose utility function is not known with certainty. Although one can engage in elicitation until a utility function is perfectly known, in practice, this is infeasible. Thus methods for decision making with imprecise utility functions are needed. We propose the use of minimax regret as an appropriate decision criterion in this circumstance, providing the means for determining robust decisions. We overview recent techniques we have developed for minimax regret computation in several different settings, with a focus of methods that exploit graphical utility models. We also describe how minimax regret can be used to drive the process of eliciting preferences itself.

Craig Boutilier is a Professor and Chair of the Department of Computer Science at the University of Toronto. He received his Ph.D. in Computer Science from the University of Toronto in 1992, and worked as an Assistant and Associate Professor at the University of British Columbia from 1991 until his return to Toronto in 1999. Boutilier was a consulting professor at Stanford University from 1998-2000, and has served on the Technical Advisory Board of CombineNet, Inc. since 2001.

Boutilier's research interests have spanned a wide range of topics, from knowledge representation, belief revision, default reasoning, and philosophical logic, to probabilistic reasoning, decision making under uncertainty, multiagent systems, and machine learning. His current research efforts focus on various aspects of decision making under uncertainty: Markov decision processes, game theory and multiagent decision processes, economic models, reinforcement learning, probabilistic inference and preference elicitation.


Nir Friedman

"Probabilistic Models for Identifying Regulation Networks: From Qualitative to Quantitative Models"

Microarray-based hybridization methods techniques allow to simultaneously measure the expression level for thousands of genes. Such measurements contain information about many different aspects of gene regulation and function, and indeed this type of experiments has become a central tool in biological research. A major computational challenge is extracting new biological understanding from this wealth of data.

Our goal is to understand the regulatory processes that bring about the observed expression patterns. This involves uncovering the structure of the interactions between genes, the function of different regulators, the mechanisms by which it influences its targets, and the dynamics of the process. Answers to these questions can come at different levels of details, depending on the available data, the modeling assumptions, and prior knowledge. In my talk, I will describe an ongoing project to use probabilistic graphical models, such as Bayesian networks and their extensions of them to model and reverse engineer regulatory networks from expression data.

I will explain the basic foundations of the approach, the model choices in defining the modeling language and in learning models from data, and methods to visualize and interpret the learned models to extract additional biological insight. I will present a progression of models that capture different aspect of gene regulation, and an assessment of their performance on several large scale yeast gene expression experiments.

This is joint work with Dana Pe'er, Iftach Nachman, Aviv Regev, Eran Segal, Micha Shapira, David Botstein, and Daphne Koller.


Tommi Jaakkola

"Information, transfer, and semi-supervised learning"


Steffen Lauritzen

"Identification and Separation of DNA Mixtures using Peak Area Information"

The lecture will describe and discuss how probabilistic expert systems can be used to analyse forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions or with models based on the gamma distribution. Such systems can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of this methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single network model and the uncertainty associated with the analysis is quantified. The lecture is almost entirely based upon joint work with Robert Cowell and Julia Mortera.

Steffen Lauritzen is educated in mathematical statistics at the University of Copenhagen and was appointed there until 1981, when he moved to the University of Aalborg to a position as Professor in Mathematics and Statistics. Recently he took up post as Professor of Statistics and Fellow of Jesus College at the University of Oxford, UK. He is mostly well known for his work on graphical models and their applications to probabilistic expert systems.


Tom Minka

"Some intuitions about message passing"

I will give an intuitive perspective of message passing algorithms,
including loopy belief propagation, generalized belief propagation,
variational message passing, and expectation propagation. I will show
how to view them in a unified way, give a feel for where they work,
and when you should use one method over another. I will also discuss
when message passing is a good way to do inference at all, and what
are the main problems with it.

Thomas Minka is a researcher at Microsoft Research Cambridge. From
2001--2003 he was a visiting assistant professor in statistics at
Carnegie Mellon University. He earned the S.B., M.Eng., and Ph.D. in
Electrical Engineering and Computer Science at MIT.

Dr Minka has published papers on methods for Bayesian inference,
document image parsing, document retrieval, and image retrieval. He
won the best paper award at SIGIR'02 and Best Pattern Recognition
Paper of 1997. He introduced the Expectation Propagation algorithm in
his dissertation, entitled "A family of algorithms for approximate
Bayesian inference."