36 citations · 50 across the 9 of their papers we have counts for
5 papers · 1 filter
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software dev…
Verifying Performance Properties of Probabilistic Inference
Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart +2
In this extended abstract, we discuss the opportunity to formally verify that inference systems for probabilistic programming guarantee good performance. In particular, we focus on…
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming
Eric Atkinson, Charles Yuan, Guillaume Baudart +2
Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on…
Programming with Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
Surrogates, models that mimic the behavior of programs, form the basis of a variety of development workflows. We study three surrogate-based design patterns, evaluating each in cas…
Programming and Reasoning with Partial Observability
Eric Atkinson, Michael Carbin
Computer programs are increasingly being deployed in partially-observable environments. A partially observable environment is an environment whose state is not completely visible t…