Andreas Stuhlmüller
Andreas Stuhlmüller
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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
Knowledge and implicature: Modeling language understanding as social cognition
ND Goodman, A Stuhlmüller
Topics in cognitive science 5 (1), 173-184, 2013
The Design and Implementation of Probabilistic Programming Languages
ND Goodman, A Stuhlmüller, 2014
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
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
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
Evaluating Compositionality in Sentence Embeddings
I Dasgupta, D Guo, A Stuhlmüller, SJ Gershman, ND Goodman
arXiv preprint arXiv:1802.04302, 2018
Learning Stochastic Inverses
A Stuhlmüller, J Taylor, N Goodman
Advances in Neural Information Processing Systems, 3048-3056, 2013
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs
A Stuhlmüller, ND Goodman
Cognitive Systems Research 28, 80-99, 2014
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
Learning physical parameters from dynamic scenes
TD Ullman, A Stuhlmüller, ND Goodman, JB Tenenbaum
Cognitive psychology 104, 57-82, 2018
RAFT: A Real-World Few-Shot Text Classification Benchmark
N Alex, E Lifland, L Tunstall, A Thakur, P Maham, CJ Riedel, E Hine, ...
arXiv preprint arXiv:2109.14076, 2021
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
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
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
D Wingate, ND Goodman, A Stuhlmüller, JM Siskind
Advances in Neural Information Processing Systems, 2011
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
Learning the Preferences of Bounded Agents
O Evans, A Stuhlmüller, ND Goodman
Advances in Neural Information Processing Systems (Bounded Optimality Workshop), 2015
Learning Structured Generative Concepts
A Stuhlmüller, JB Tenenbaum, ND Goodman
Proceedings of the Thirty-Second Annual Conference of the Cognitive Science …, 2010
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
Inducing Probabilistic Programs by Bayesian Program Merging
I Hwang, A Stuhlmüller, ND Goodman
Arxiv preprint arXiv:1110.5667, 2011
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