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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…
On the (In)Tractability of Reinforcement Learning for LTL Objectives
Cambridge Yang, Michael Littman, Michael Carbin
In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objecti…
Statically Bounded-Memory Delayed Sampling for Probabilistic Streams
Eric Atkinson, Guillaume Baudart, Louis Mandel +2
Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated…
Studying the Consistency and Composability of Lottery Ticket Pruning Masks
Rajiv Movva, Jonathan Frankle, Michael Carbin
Magnitude pruning is a common, effective technique to identify sparse subnetworks at little cost to accuracy. In this work, we ask whether a particular architecture's accuracy-spar…