490 citations · 1k across the 74 of their papers we have counts for
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SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka
We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inf…
Automating Involutive MCMC using Probabilistic and Differentiable Programming
Marco Cusumano-Towner, Alexander K. Lew, Vikash K. Mansinghka
Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels ba…
Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty, Kenta Takatsu, David Jensen +1
Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects. In some cases, these confounders are shared across obser…
Deep Involutive Generative Models for Neural MCMC
Span Spanbauer, Cameron Freer, Vikash Mansinghka
We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC.…
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Alexander K. Lew, Monica Agrawal, David Sontag +1
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the ha…
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver +2
People routinely infer the goals of others by observing their actions over time. Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when…