38 citations · 149 across the 27 of their papers we have counts for
6 papers · 1 filter
Evaluating and Optimizing Educational Content with Large Language Model Judgments
Joy He-Yueya, Noah D. Goodman, Emma Brunskill
Creating effective educational materials generally requires expensive and time-consuming studies of student learning outcomes. To overcome this barrier, one idea is to build comput…
Generating Language Corrections for Teaching Physical Control Tasks
Megha Srivastava, Noah Goodman, Dorsa Sadigh
AI assistance continues to help advance applications in education, from language learning to intelligent tutoring systems, yet current methods for providing students feedback are s…
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…
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…
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…