logo

My PhD Thesis:

Exponential Family Predictive Representations of State
Publications:
Automated Variational Inference in Probabilistic Programming
David Wingate, Theo Weber

Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
David Wingate, Noah D. Goodman, Andreas Stuhlmueller, and Jeffrey M. Siskind
Neural Information Processing Systems (NIPS), 2011.
      Here's the video of the Bayesian mesh induction from the paper.

Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation
David Wingate, Andreas Stuhlmueller, and Noah D. Goodman
Artificial Intelligence and Statistics (AISTATS), 2011.

Bayesian Policy Search with Policy Priors
David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie P. Kaelbling and Joshua B. Tenenbaum
International Joint Conference on Artificial Intelligence (IJCAI), 2011. Best poster award.
      Some neat supporting videos are also available.

Infinite Dynamic Bayesian Networks
Finale Doshi-Velez, David Wingate, Nicholas Roy and Joshua B. Tenenbaum
International Conference on Machine Learning (ICML), 2011.

Smart Data Structures: An Online Machine Learning Approach to Multicore Data Structures
Jonathan Eastep, David Wingate and Anant Agarwal
IEEE International Conference on Autonomic Computing and Communications (ICAC), 2011.
      Code is available at the Smart Data Structures github repository.

Nonparametric Bayesian Policy Priors for Reinforcement Learning
Finale Doshi-Velez, David Wingate, Nicholas Roy and Joshua B. Tenenbaum
Neural Information Processing Systems (NIPS), 2010.

Smartlocks: Lock Acquisition Scheduling for Self-Aware Synchronization
Jonathan Eastep, David Wingate, Marco D. Santambrogio, Anant Agarwal
IEEE International Conference on Autonomic Computing and Communications (ICAC), 2010. Best paper award.

The Infinite Latent Events Model
David Wingate, Noah D. Goodman, Daniel M. Roy and Joshua B. Tenenbaum
Uncertainty in Artificial Intelligence (UAI), 2009.

A Bayesian Sampling Approach to Exploration in Reinforcement Learning
John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri and David Wingate,
Uncertainty in Artificial Intelligence (UAI), 2009.

Efficiently Learning Linear-Linear Exponential Family Predictive Representations of State
David Wingate and Satinder Singh
International Conference on Machine Learning (ICML), 2008.

Sigma Point Policy Iteration
Michael Bowling, Alborz Geramifard and David Wingate
Autonomous Agents and Multiagent Systems (AAMAS), 2008.

Exponential Family Predictive Representations of State
David Wingate and Satinder Singh
Neural Information Processing Systems (NIPS), 2007.

On Discovery and Learning of Models with Predictive Representations of State for Agents with Continuous Actions and Observations
David Wingate and Satinder Singh
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1128-1135, 2007.

Relational Knowledge with Predictive State Representations
David Wingate, Vishal Soni, Britton Wolfe and Satinder Singh
International Joint Conference on Artificial Intelligence (IJCAI), pages 2035-2040, 2007.

Mixtures of Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems
David Wingate and Satinder Singh
National Conference on Artificial Intelligence (AAAI), 2006.

Kernel Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems
David Wingate and Satinder Singh
International Conference on Machine Learning (ICML), pages 1017 - 1024, 2006.

Prioritization Methods for Accelerating MDP Solvers
David Wingate and Kevin D. Seppi
Journal of Machine Learning Research (JMLR) 6(May):851-881, 2005.

Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Matt Rudary, Satinder Singh and David Wingate
Uncertainty in Artificial Intelligence (UAI), pages 501-508, 2005.

Prioritized Multiplicative Schwarz Procedures for Solutions to General Linear Systems
David Wingate, Nathaniel Powell, Quinn Snell and Kevin D. Seppi
International Parallel and Distributed Processing Symposium (IPDPS), 2005.

P3VI: A Partitioned, Prioritized, Parallel Value Iterator
David Wingate and Kevin D. Seppi
International Conference on Machine Learning (ICML), pages 863-870, 2004.

Variable Resolution Discretization in the Joint Space
Christopher K. Monson, David Wingate, Kevin D. Seppi, and Todd S. Peterson
International Conference on Machine Learning and Applications, 2004.

Efficient Value Iteration Using Partitioned Models
David Wingate and Kevin D. Seppi
International Conference on Machine Learning and Applications, pages 53-59, 2003.
Best paper award.

Workshop Publications:
Smartlocks: Self-Aware Synchronization through Lock Acquisition Scheduling
Jonathan Eastep, David Wingate, Marco D. Santambrogio, and Anant Agarwal
4th Workshop on Statistical and Machine learning approaches to Architecture and Compilation (SMART'10), 2010.

Cache Performance of Priority Metrics for MDP Solvers
David Wingate and Kevin D. Seppi
AAAI Workshop on Learning and Planning in Markov Processes, pages 103-106, 2004.

My Master's Thesis:
Solving Large MDPs Quickly with Partitioned Value Iteration

Some neat videos and source code are available.

Organized Events:
I'm helping to organize a AAAI Spring Symposium called Designing Intelligent Robots: Reintegrating AI with George Konidaris, Sarah Osentoski, Todd Hester, Stephen Hart and Byron Boots. Check it out!
Finale Doshi-Velez and I co-organized the ICML 2011 Workshop on Planning and Acting with Uncertain Models.
I was the general chair of the 2009 Reinforcement Learning Competition.
Brian Tanner, Michael James and I co-organized the NIPS 2006 Workshop on Grounding Perception, Knowledge and Cognition in Sensori-Motor Experience.

 
The top of a fluffy cloud.