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Andreas Stuhlmüller
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2020 – today
- 2023
- [i12]Justin Reppert, Ben Rachbach, Charlie George, Luke Stebbing, JungWon Byun, Maggie Appleton, Andreas Stuhlmüller:
Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes. CoRR abs/2301.01751 (2023) - [i11]Charlie George, Andreas Stuhlmüller:
Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers. CoRR abs/2310.10627 (2023) - 2022
- [i10]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, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, et al.:
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. CoRR abs/2206.04615 (2022) - 2021
- [c13]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:
RAFT: A Real-World Few-Shot Text Classification Benchmark. NeurIPS Datasets and Benchmarks 2021 - [i9]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:
RAFT: A Real-World Few-Shot Text Classification Benchmark. CoRR abs/2109.14076 (2021)
2010 – 2019
- 2018
- [c12]William Saunders, Girish Sastry, Andreas Stuhlmüller, Owain Evans:
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention. AAMAS 2018: 2067-2069 - [c11]Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel Gershman, Noah D. Goodman:
Evaluating Compositionality in Sentence Embeddings. CogSci 2018 - [i8]Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J. Gershman, Noah D. Goodman:
Evaluating Compositionality in Sentence Embeddings. CoRR abs/1802.04302 (2018) - 2017
- [i7]David Abel, John Salvatier, Andreas Stuhlmüller, Owain Evans:
Agent-Agnostic Human-in-the-Loop Reinforcement Learning. CoRR abs/1701.04079 (2017) - [i6]William Saunders, Girish Sastry, Andreas Stuhlmüller, Owain Evans:
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention. CoRR abs/1707.05173 (2017) - 2016
- [c10]Owain Evans, Andreas Stuhlmüller, Noah D. Goodman:
Learning the Preferences of Ignorant, Inconsistent Agents. AAAI 2016: 323-329 - [c9]Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman:
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. AISTATS 2016: 28-37 - 2015
- [c8]Robert X. D. Hawkins, Andreas Stuhlmüller, Judith Degen, Noah D. Goodman:
Why do you ask? Good questions provoke informative answers. CogSci 2015 - [i5]Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman:
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. CoRR abs/1509.02151 (2015) - [i4]Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth, Noah D. Goodman:
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs. CoRR abs/1509.02962 (2015) - [i3]Owain Evans, Andreas Stuhlmüller, Noah D. Goodman:
Learning the Preferences of Ignorant, Inconsistent Agents. CoRR abs/1512.05832 (2015) - 2014
- [j2]Andreas Stuhlmüller, Noah D. Goodman:
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs. Cogn. Syst. Res. 28: 80-99 (2014) - [c7]Tomer D. Ullman, Andreas Stuhlmüller, Noah D. Goodman, Joshua B. Tenenbaum:
Learning physical theories from dynamical scenes. CogSci 2014 - 2013
- [j1]Noah D. Goodman, Andreas Stuhlmüller:
Knowledge and Implicature: Modeling Language Understanding as Social Cognition. Top. Cogn. Sci. 5(1): 173-184 (2013) - [c6]Andreas Stuhlmüller, Jessica Taylor, Noah D. Goodman:
Learning Stochastic Inverses. NIPS 2013: 3048-3056 - 2012
- [c5]Noah D. Goodman, Andreas Stuhlmüller:
Knowledge and implicature: Modeling language understanding as social cognition. CogSci 2012 - [c4]John McCoy, Tomer D. Ullman, Andreas Stuhlmüller, Tobias Gerstenberg, Joshua B. Tenenbaum:
Probabilistic generative models for counterfactual reasoning and blame attribution. CogSci 2012 - [c3]Andreas Stuhlmüller, Noah D. Goodman:
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. StarAI@UAI 2012 - [i2]Andreas Stuhlmüller, Noah D. Goodman:
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. CoRR abs/1206.3555 (2012) - 2011
- [c2]David Wingate, Noah D. Goodman, Andreas Stuhlmüller, Jeffrey Mark Siskind:
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. NIPS 2011: 1152-1160 - [c1]David Wingate, Andreas Stuhlmüller, Noah D. Goodman:
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. AISTATS 2011: 770-778 - [i1]Irvin Hwang, Andreas Stuhlmüller, Noah D. Goodman:
Inducing Probabilistic Programs by Bayesian Program Merging. CoRR abs/1110.5667 (2011)
Coauthor Index
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