14 citations · 53 across the 12 of their papers we have counts for
12 papers
Building Machines that Learn and Think with People
Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt +10
What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and t…
Probabilistic Programming with Programmable Variational Inference
McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang +4
Compared to the wide array of advanced Monte Carlo methods supported by modern probabilistic programming languages (PPLs), PPL support for variational inference (VI) is less develo…
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables
Mathieu Huot, Matin Ghavami, Alexander K. Lew +6
This article presents GenSQL, a probabilistic programming system for querying probabilistic generative models of database tables. By augmenting SQL with only a few key primitives f…
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka +1
People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assist…
Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman +2
This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian nonparametric prior over a symbolic space of…
PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka +1
We introduce a new setting, the category of PAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages. Our semantics is ge…