
Keynote Speakers
ISAIM 2008 features three distinguished invited speakers:
Titles and abstracts of invited talks:
David McAllester

Toyota Technological Institute at Chicago

Mathematical Problems from Machine Learning
Machine learning and statistical inference have become
foundational in most applications of computer science, e.g.,
computational biology, speech recognition, computer vision, natural
language translation, electronic commerce, database integretiy, and
information retrieval. But the mathematical foundations of machine
learning and statistical inference remain largely unresolved. Most
glaringly, the generalization properties of simple least squares
regression (Tychonoff regression) remain poorly understood  existing
bounds do not work well for the common case of a Gaussian kernel. The
generalization theory of simple support vector machines (SVMs) and SVMs
with latent variables or structured labels are even less well
understood. The choice of regularization (L2, L1, or L0) is perhaps the
least well understood. This talk will focus on open mathematical
problems but will also discuss the philosophical foundations of
generalizing from finite data.


Francesca Rossi

University of Padova

Preferences, Constraints, Uncertainty, and MultiAgent Scenarios
Preferences occur in many everyday tasks.
Whether we look for a house, a car, a computer,
a digital camera, or a vacation package,
we usually state our own preferences and
we expect to find the item of our dreams.
It is therefore natural that modelling and solving sets of
preferences is a central issue in AI,
if we want to understand human intelligence and
use computing devices to replicate some of functions of the human brain.
This talk will discuss different kinds of preferences,
it will describe and compare some of the AI formalisms to model
preferences, and it will hint at existing preference solvers.
Uncertainty will also be considered, in the form of a possibly
incomplete set of preferences, because of privacy issues or missing data.
We will also discuss multiagent settings where
possibly incomplete preferences need to be aggregated, and will
present results related to both normative and
computational properties of such systems.
While the results on singleagent preference solving
are mostly related to AI subareas such as
constraint programming and knowledge representation,
those on multiagent preference aggregation
are multidisciplinary, since preference aggregation and
its properties have been extensively studied also in
in decision theory, economy, and political sciences.


Naftali Tishby

Hebrew University

Extracting Relevant Information from Samples
A ubiquitous property of intelligent systems is their ability to separate
the essential from the irrelevant. A precise mathematical formulation of
this fundamental ability remains elusive, since it is conceptualized in
different ways in different scientific fields. In classical pattern
recognition, this idea is rendered as finding "good features", or data
representations that can separate "signal from the noise". In parametric
statistics, the notion of relevant information in a sample is captured
in Fischer's definition of "minimal sufficient statistics", which in its original
form was in fact restricted to parametric families in exponential
forms. In machine learning, this same aim is captured in algorithms
that produce useful hypotheses from samples, and are tested by their
ability to generalize beyond the given data.
In this talk I will argue that all these views can be unified by one
information theoretic tradeoff between compression and prediction,
known as "The Information Bottleneck method". This surprisingly simple
principle, found in entirely different contexts in information theory,
naturally generalizes the classical notion of minimal sufficient
statistics into a continuum of efficient predictive representations,
which can be effectively calculated. I will present some new rigorous
properties of the information bottleneck method, and more specifically
will discuss recently obtained generalization and distribution
independent finite sample bounds. 
