2.3k citations · 4.7k across the 88 of their papers we have counts for
15 papers · 1 filter
CLEVRER-Humans: Describing Physical and Causal Events the Human Way
Jiayuan Mao, Xuelin Yang, Xikun Zhang +2
Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical…
Strategic Reasoning with Language Models
Kanishk Gandhi, Dorsa Sadigh, Noah D. Goodman
Strategic reasoning enables agents to cooperate, communicate, and compete with other agents in diverse situations. Existing approaches to solving strategic games rely on extensive…
Why think step by step? Reasoning emerges from the locality of experience
Ben Prystawski, Michael Y. Li, Noah D. Goodman
Humans have a powerful and mysterious capacity to reason. Working through a set of mental steps enables us to make inferences we would not be capable of making directly even though…
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Atticus Geiger, Zhengxuan Wu, Christopher Potts +2
Causal abstraction is a promising theoretical framework for explainable artificial intelligence that defines when an interpretable high-level causal model is a faithful simplificat…
Peano: Learning Formal Mathematical Reasoning
Gabriel Poesia, Noah D. Goodman
General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent gene…
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions
Zhening Li, Gabriel Poesia, Omar Costilla-Reyes +2
Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thu…
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
Gabriel Poesia, WenXin Dong, Noah Goodman
Abstract symbolic reasoning, as required in domains such as mathematics and logic, is a key component of human intelligence. Solvers for these domains have important applications,…
Applying Probabilistic Programming to Affective Computing
Desmond C. Ong, Harold Soh, Jamil Zaki +1
Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emot…
Deep Amortized Inference for Probabilistic Programs
Daniel Ritchie, Paul Horsfall, Noah D. Goodman
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference i…
Practical optimal experiment design with probabilistic programs
Long Ouyang, Michael Henry Tessler, Daniel Ly +1
Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could ru…
Learning the Preferences of Ignorant, Inconsistent Agents
Owain Evans, Andreas Stuhlmueller, Noah D. Goodman
An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each cas…
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth +1
Many practical techniques for probabilistic inference require a sequence of distributions that interpolate between a tractable distribution and an intractable distribution of inter…
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman
Lightweight, source-to-source transformation approaches to implementing MCMC for probabilistic programming languages are popular for their simplicity, support of existing determini…
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
Andreas Stuhlmüller, Noah D. Goodman
We describe a dynamic programming algorithm for computing the marginal distribution of discrete probabilistic programs. This algorithm takes a functional interpreter for an arbitra…
Inducing Probabilistic Programs by Bayesian Program Merging
Irvin Hwang, Andreas Stuhlmüller, Noah D. Goodman
This report outlines an approach to learning generative models from data. We express models as probabilistic programs, which allows us to capture abstract patterns within the examp…