Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Holtzen, Steven"'
Currently, there is a gap between the tools used by probability theorists and those used in formal reasoning about probabilistic programs. On the one hand, a probability theorist decomposes probabilistic state along the simple and natural product of
Externí odkaz:
http://arxiv.org/abs/2405.06826
Programmatically generating tight differential privacy (DP) bounds is a hard problem. Two core challenges are (1) finding expressive, compact, and efficient encodings of the distributions of DP algorithms, and (2) state space explosion stemming from
Externí odkaz:
http://arxiv.org/abs/2402.16982
Probabilistic programming languages (PPLs) are expressive means for creating and reasoning about probabilistic models. Unfortunately hybrid probabilistic programs, involving both continuous and discrete structures, are not well supported by today's P
Externí odkaz:
http://arxiv.org/abs/2312.05706
Autor:
Cao, William X., Garg, Poorva, Tjoa, Ryan, Holtzen, Steven, Millstein, Todd, Broeck, Guy Van den
Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs). The core challenge comes from discrete structure: many of today's PPL inference strategies rely
Externí odkaz:
http://arxiv.org/abs/2307.13837
TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated type prediction: given an untyped program, produce a well-typed output pr
Externí odkaz:
http://arxiv.org/abs/2305.17145
We present Lilac, a separation logic for reasoning about probabilistic programs where separating conjunction captures probabilistic independence. Inspired by an analogy with mutable state where sampling corresponds to dynamic allocation, we show how
Externí odkaz:
http://arxiv.org/abs/2304.01339
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precise way in which the probabilistic program is written. A standard way of
Externí odkaz:
http://arxiv.org/abs/2110.10284
Autor:
Holtzen, Steven, Junges, Sebastian, Vazquez-Chanlatte, Marcell, Millstein, Todd, Seshia, Sanjit A., Broeck, Guy Van Den
We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inf
Externí odkaz:
http://arxiv.org/abs/2105.12326
Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are
Externí odkaz:
http://arxiv.org/abs/2103.17226
Scaling probabilistic models to large realistic problems and datasets is a key challenge in machine learning. Central to this effort is the development of tractable probabilistic models (TPMs): models whose structure guarantees efficient probabilisti
Externí odkaz:
http://arxiv.org/abs/2006.15233