| Knowledge and implicature: Modeling language understanding as social cognition ND Goodman, A Stuhlmüller Topics in cognitive science 5 (1), 173-184, 2013 | 292 | 2013 |
| The Design and Implementation of Probabilistic Programming Languages ND Goodman, A Stuhlmüller http://dippl.org, 2014 | 154 | 2014 |
| Lightweight implementations of probabilistic programming languages via transformational compilation D Wingate, A Stuhlmüller, ND Goodman International Conference on Artificial Intelligence and Statistics, 770-778, 2011 | 148 | 2011 |
| Learning Stochastic Inverses A Stuhlmüller, J Taylor, N Goodman Advances in Neural Information Processing Systems, 3048-3056, 2013 | 77 | 2013 |
| Learning the Preferences of Ignorant, Inconsistent Agents O Evans, A Stuhlmüller, ND Goodman Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-2016), 2016 | 54 | 2016 |
| Trial without Error: Towards Safe Reinforcement Learning via Human Intervention W Saunders, G Sastry, A Stuhlmueller, O Evans Proceedings of the 17th International Conference on Autonomous Agents and …, 2018 | 51 | 2018 |
| Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs A Stuhlmüller, ND Goodman Cognitive Systems Research 28, 80-99, 2014 | 46 | 2014 |
| Nonstandard Interpretations of Probabilistic Programs for Efficient Inference D Wingate, ND Goodman, A Stuhlmüller, JM Siskind Advances in Neural Information Processing Systems, 2011 | 34 | 2011 |
| Learning physics from dynamical scenes T Ullman, A Stuhlmüller, N Goodman, JB Tenenbaum Proceedings of the 36th Annual Conference of the Cognitive Science Society …, 2014 | 30 | 2014 |
| Evaluating Compositionality in Sentence Embeddings I Dasgupta, D Guo, A Stuhlmüller, SJ Gershman, ND Goodman arXiv preprint arXiv:1802.04302, 2018 | 29 | 2018 |
| A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs A Stuhlmüller, ND Goodman Second Statistical Relational AI workshop at UAI 2012 (StaRAI-12), 2012 | 24 | 2012 |
| Learning Structured Generative Concepts A Stuhlmüller, JB Tenenbaum, ND Goodman Proceedings of the Thirty-Second Annual Conference of the Cognitive Science …, 2010 | 20 | 2010 |
| Agent-Agnostic Human-in-the-Loop Reinforcement Learning D Abel, J Salvatier, A Stuhlmüller, O Evans Future of Interactive Learning Machines Workshop at NIPS 2016, 2016 | 19 | 2016 |
| Learning the Preferences of Bounded Agents O Evans, A Stuhlmüller, ND Goodman Advances in Neural Information Processing Systems (Bounded Optimality Workshop), 2015 | 19 | 2015 |
| C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching D Ritchie, A Stuhlmüller, ND Goodman Proceedings of the 19th International Conference on Artificial Intelligence …, 2016 | 18 | 2016 |
| Why do you ask? Good questions provoke informative answers. RXD Hawkins, A Stuhlmüller, J Degen, ND Goodman Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 | 17 | 2015 |
| Inducing Probabilistic Programs by Bayesian Program Merging I Hwang, A Stuhlmüller, ND Goodman Arxiv preprint arXiv:1110.5667, 2011 | 15 | 2011 |
| Learning physical parameters from dynamic scenes TD Ullman, A Stuhlmüller, ND Goodman, JB Tenenbaum Cognitive psychology 104, 57-82, 2018 | 12 | 2018 |
| Why blame Bob? Probabilistic generative models, counterfactual reasoning, and blame attribution J McCoy, T Ullman, A Stuhlmüller, T Gerstenberg, JB Tenenbaum Thirty-Fourth Annual Conference of the Cognitive Science Society, 2012 | 8 | 2012 |
| Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs A Stuhlmüller, RXD Hawkins, N Siddharth, ND Goodman arXiv preprint arXiv:1509.02962, 2015 | 4 | 2015 |
Noah D. GoodmanStanford UniversityVerified email at stanford.edu
