Probabilistic Programming is a way of defining probabilistic models by overloading the operations in standard programming language to have probabilistic meanings. The goal is to specify probabilistic ...

We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop ...

Abstract: This paper proposes a distributed model predictive control (DMPC) for a class of discrete-time stochastic multi-agent systems subject to partially coupled temporal logic tasks. For each ...

Artificial intelligence and machine learning could become dramatically more efficient, thanks to a new type of computer component developed by researchers at the University of California, Santa ...

Abstract: Probabilistic graphical models are a fundamen-tal tool for modeling uncertainty and statistical dependencies in various domains, making them indispensable for decision-making, machine ...