Anton Chechetka

PhD student Office: 214 Smith Hall
Robotics Institute Email: antonc at cs dot cmu dot edu
Carnegie Mellon University Tel: (412) 268-1858

I am a 6th year graduate student at the Robotics Institute advised by Carlos Guestrin. My research interests are in machine learning and probabilistic inference. For the first 2.5 years of the PhD program I was advised by Katia Sycara and worked on algorithms for distributed constraint optimization.


I am interested in principled ways to construct probabilistic models that accurately represent reality and at the same time are feasible for exact inference. More specifically, I am working on learning thin junction trees from data.


Previous research

Distributed constraint optimization is a way to formalize the problem of coordination in the group of cooperative agents (for example, robots, people, or sensor nodes). Each agent has exclusive control over one variable and the group has to jointly select an assignment for these variables (e.g. come up with a joint meetings schedule) so as to maximize performance (sum of the values of constraints). Each constraint is a real-valued function over a subset of variables. The agents communicate with each other in order to agree on the jointly optimal assignment.

A popular basic method for solving such problems is multiagent search. To speed up the solution process, we exploit the distributed aspect of the problem by having different agents explore non-intersecting regions of the search space simultaneously. This technique reduces synchronization overhead and makes pruning of the search space faster.



In Spring'06 I TA'd 10-701/15-781 Machine Learning class.


Here is the list of classes I have taken at CMU so far.