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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…