Matthew W. Hoffman¶
I am currently a graduate student in the CS department at UBC, supervised by Nando de Freitas and Arnaud Doucet. I’m working on various approaches to reformulate stochastic control problems as inference problems, particularly in large, continuous domains.
You can also take a look at my CV, although this may be horribly out of date.
KAIST ML Tutorial
In a recent tutorial for KAIST entitled “Reinforcement learning and planning”, I made a few quick demos of dynamic programming and reinforcement learning methods, which can be found here. My slides for this presentation can be found here.
Recent news
In the run-up to my soon-to-be-coming graduation I’ve been collecting all of my code and making that available, all of which you can find on the Software page.
Publications¶
- M. Hoffman, A. Lazaric, M. Ghavamzadeh, R. Munos. Regularized Least Squares Temporal Difference learning with nested l2 and l1 penalization. EWRL, 2011.
- M. Hoffman, E. Brochu, N. de Freitas. Portfolio Allocation for Bayesian Optimization. UAI, 2011.
- M. Ghavamzadeh, A. Lazaric, R. Munos, M. Hoffman. Finite sample analysis of Lasso-TD. ICML, 2011.
- M. Hoffman and N. de Freitas. Inference strategies for solving semi-Markov decision processes. To appear in Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, L.E. Sucar, E. Morales, H. Hoey (Eds.), 2010.
- M. Hoffman, P. Carbonetto, N. de Freitas, and A. Doucet. Inference strategies for solving semi-Markov decision processes. NIPS Workshop on Probabilistic Approaches for Robotics and Control, 2009.
- M. Hoffman, H. Kueck, N. de Freitas, A. Doucet. New inference strategies for solving Markov Decision Processes using reversible jump MCMC. UAI, 2009.
- H. Kueck, M. Hoffman, A. Doucet, N. de Freitas. Inference and Learning for Active Sensing, Experimental Design, and Control. Invited paper, IBPRIA, 2009.
- M. Hoffman, N. de Freitas, A. Doucet, Jan Peters. An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Rewards. AISTATS, 2009. (errata).
- M. Hoffman, A. Doucet, N. de Freitas, and A. Jasra. Bayesian policy learning with trans-dimensional MCMC. NIPS, 2007. (This is an extended and greatly revised version of UBC CS TR-2007-04.)
- M. Hoffman, A. Doucet, N. de Freitas, and A. Jasra. On Solving General State-Space Sequential Decision Problems using Inference Algorithms. UBC CS TR-2007-04. March, 2007.
Older publications¶
In a past life I was an undergraduate in the CS and Math departments at the University of Washington. While there I worked with Rajesh Rao as part of the Neural Systems Group, and I focused primarily on problems of gaze-imitation and imitation-learning utilizing shared-attention. For further information see the relevant publications:
- M. Hoffman, D. Grimes, A. Shon, and R. Rao. A probabilistic model of gaze imitation and shared attention. Neural Networks 19(3): Special Issue on Brain Mechanisms of Imitation; 2006.
- A. Shon, D. Grimes, C. Baker, M. Hoffman, S. Zhou, R. Rao. Probabilistic gaze imitation and saliency learning in a robotic head. ICRA, 2005.