# Colloquium

## Parallelizing Programs using Approximate Code

## Estimation Problems in Machine Learning

## Learning Theory and Problems of Statistics

Along with a survey of new ideas of statistical learning theory and their comparison to classical statistical approaches, I will discuss the problem of constructing learning machines that implement these new ideas.

## Automatic Tools for Building Secure Systems

## Event (no name)

## A Signal-Processing Framework for Forward and Inverse Rendering

## Programming for Pervasive Computing Environments

## Digital Voices

As any other modems, the design of these "air modems" must account for factors such as the data rate, the error probability and the computational overhead at the receiver. On top of that, these modems must also account for aesthetic and psychoacoustic factors. I will show how to vary certain parameters in standard modulation techniques such as ASK, FSK and Spread-Spectrum to obain communication systems in which the messages are musical and other familiar sounds, rather than modem sounds. Some applications of this technology will be discussed. I will also present the basis of a framework for studying low bit rate communication systems including air modems, bird songs, and human speach.

## Sparse Sampling Algorithms for Probabilistic Artificial Intelligence

Over the past several years, we have been systematically exploring the application of this fundamental notion to a broader set of natural problems in probabilistic artificial intelligence, and have found a number of striking examples where it leads to novel algorithms and analyses for core problems. In particular, by applying uniform convergence arguments to carefully structured but very sparse samples in probabilistic environments, we obtain:

* A new algorithm providing a near-optimal solution to the exploration-exploitation trade-off in Markov decision processes, a basic problem in reinforcement learning;

* A sparse sampling algorithm for planning in Markov decision processes whose running time has no dependence on the number of states;

* Powerful new algorithms for probabilistic inference in Bayesian networks:

*Algorithms for computing approximate Nash equilibria in large-population games.

These are only a few of the many algorithmic applications of learning theory methods in modern AI. This talk will present a self contained survey of these developments.