112 citations · 228 across the 11 of their papers we have counts for
6 papers · 1 filter
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
William Saunders, Girish Sastry, Andreas Stuhlmueller +1
AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to a…
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…