Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. - David Wingate; Noah D. Goodman; Andreas Stuhlmüller; Jeffrey Mark Siskind (NIPS, 2011)

Inducing Probabilistic Programs by Bayesian Program Merging - Irvin Hwang; Andreas Stuhlmüller; Noah D. Goodman (CoRR, 2011)

Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. - David Wingate; Andreas Stuhlmüller; Noah D. Goodman (AISTATS, 2011)

Probabilistic generative models for counterfactual reasoning and blame attribution. - John McCoy; Tomer D. Ullman; Andreas Stuhlmüller; Tobias Gerstenberg; Joshua B. Tenenbaum (CogSci, 2012)

A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. - Andreas Stuhlmüller; Noah D. Goodman (StarAI@UAI, 2012)

Learning Stochastic Inverses. - Andreas Stuhlmüller; Jessica Taylor; Noah D. Goodman (NIPS, 2013)

Knowledge and Implicature: Modeling Language Understanding as Social Cognition. - Noah D. Goodman; Andreas Stuhlmüller (Top. Cogn. Sci., 2013)

Learning physical theories from dynamical scenes. - Tomer D. Ullman; Andreas Stuhlmüller; Noah D. Goodman; Joshua B. Tenenbaum (CogSci, 2014)

Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs. - Andreas Stuhlmüller; Noah D. Goodman (Cogn. Syst. Res., 2014)

Why do you ask? Good questions provoke informative answers. - Robert X. D. Hawkins; Andreas Stuhlmüller; Judith Degen; Noah D. Goodman (CogSci, 2015)

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs. - Andreas Stuhlmüller; Robert X. D. Hawkins; N. Siddharth 001; Noah D. Goodman (CoRR, 2015)

Learning the Preferences of Ignorant, Inconsistent Agents. - Owain Evans; Andreas Stuhlmüller; Noah D. Goodman (AAAI, 2016)

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. - Daniel Ritchie; Andreas Stuhlmüller; Noah D. Goodman (AISTATS, 2016)

Agent-Agnostic Human-in-the-Loop Reinforcement Learning. - David Abel; John Salvatier; Andreas Stuhlmüller; Owain Evans (CoRR, 2017)

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention. - William Saunders; Girish Sastry; Andreas Stuhlmüller; Owain Evans (AAMAS, 2018)

Evaluating Compositionality in Sentence Embeddings. - Ishita Dasgupta 001; Demi Guo; Andreas Stuhlmüller; Samuel Gershman; Noah D. Goodman (CogSci, 2018)

RAFT: A Real-World Few-Shot Text Classification Benchmark. - Neel Alex; Eli Lifland; Lewis Tunstall; Abhishek Thakur; Pegah Maham; C. Jess Riedel; Emmie Hine; Carolyn Ashurst; Paul Sedille; Alexis Carlier; Michael Noetel; Andreas Stuhlmüller (NeurIPS Datasets and Benchmarks, 2021)

Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes. - Justin Reppert; Ben Rachbach; Charlie George; Luke Stebbing; JungWon Byun; Maggie Appleton; Andreas Stuhlmüller (CoRR, 2023)

Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers. - Charlie George; Andreas Stuhlmüller (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)