David Wingate
Title
Cited by
Cited by
Year
A Bayesian sampling approach to exploration in reinforcement learning
J Asmuth, L Li, ML Littman, A Nouri, D Wingate
arXiv preprint arXiv:1205.2664, 2012
1642012
Lightweight implementations of probabilistic programming languages via transformational compilation
D Wingate, A Stuhlmüller, N Goodman
Proceedings of the Fourteenth International Conference on Artificial …, 2011
1572011
Prioritization methods for accelerating MDP solvers
D Wingate, KD Seppi
Journal of Machine Learning Research 6 (May), 851-881, 2005
962005
Automated variational inference in probabilistic programming
D Wingate, T Weber
arXiv preprint arXiv:1301.1299, 2013
822013
Acoustic source tracking and selection
D Wingate, ND Stein, B Vigoda, P Ohiomoba, B Donnelly
US Patent App. 14/847,818, 2016
592016
What can you do with a rock? affordance extraction via word embeddings
N Fulda, D Ricks, B Murdoch, D Wingate
arXiv preprint arXiv:1703.03429, 2017
482017
A physics-based model prior for object-oriented mdps
J Scholz, M Levihn, C Isbell, D Wingate
International Conference on Machine Learning, 1089-1097, 2014
472014
Nonparametric Bayesian policy priors for reinforcement learning
F Doshi-Velez, D Wingate, N Roy, JB Tenenbaum
Advances in Neural Information Processing Systems, 532-540, 2010
392010
Infinite dynamic Bayesian networks
F Doshi-Velez, D Wingate, J Tenenbaum, N Roy
International Machine Learning Society, 2011
372011
Nonstandard interpretations of probabilistic programs for efficient inference
D Wingate, N Goodman, A Stuhlmüller, JM Siskind
Advances in Neural Information Processing Systems, 1152-1160, 2011
352011
Smartlocks: lock acquisition scheduling for self-aware synchronization
J Eastep, D Wingate, MD Santambrogio, A Agarwal
Proceedings of the 7th international conference on Autonomic computing, 215-224, 2010
332010
Bayesian policy search with policy priors
D Wingate, ND Goodman, DM Roy, LP Kaelbling, JB Tenenbaum
Twenty-Second International Joint Conference on Artificial Intelligence, 2011
312011
Kernel predictive linear Gaussian models for nonlinear stochastic dynamical systems
D Wingate, S Singh
Proceedings of the 23rd international conference on Machine learning, 1017-1024, 2006
302006
Learning nonlinear dynamic models of soft robots for model predictive control with neural networks
MT Gillespie, CM Best, EC Townsend, D Wingate, MD Killpack
2018 IEEE International Conference on Soft Robotics (RoboSoft), 39-45, 2018
282018
Predictive linear-Gaussian models of stochastic dynamical systems
M Rudary, S Singh, D Wingate
arXiv preprint arXiv:1207.1416, 2012
282012
On discovery and learning of models with predictive representations of state for agents with continuous actions and observations
D Wingate, S Singh
Proceedings of the 6th international joint conference on Autonomous agents …, 2007
282007
Smart data structures: an online machine learning approach to multicore data structures
J Eastep, D Wingate, A Agarwal
Proceedings of the 8th ACM international conference on Autonomic computing …, 2011
262011
P3VI: A partitioned, prioritized, parallel value iterator
D Wingate, KD Seppi
Proceedings of the twenty-first international conference on Machine learning …, 2004
252004
Exponential family predictive representations of state
D Wingate, SS Baveja
Advances in Neural Information Processing Systems, 1617-1624, 2008
242008
Signal source separation
D Wingate, N Stein
US Patent 9,460,732, 2016
232016
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