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of 169
pro vyhledávání: '"Millstein, Todd"'
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
We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, producing promising draft configurations but with egregious errors in topolo
Externí odkaz:
http://arxiv.org/abs/2307.04945
Autor:
Tang, Alan, Beckett, Ryan, Benaloh, Steven, Jayaraman, Karthick, Patil, Tejas, Millstein, Todd, Varghese, George
Publikováno v:
In Proceedings of the ACM SIGCOMM 2023 Conference (ACM SIGCOMM '23). Association for Computing Machinery, New York, NY, USA, 94-107
Current network control plane verification tools cannot scale to large networks, because of the complexity of jointly reasoning about the behaviors of all nodes in the network. In this paper we present a modular approach to control plane verification
Externí odkaz:
http://arxiv.org/abs/2204.09635
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
The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned function is intended to satisfy domain-specific constraints. We focus on mo
Externí odkaz:
http://arxiv.org/abs/2006.08852
Publikováno v:
Proc. ACM Program. Lang. 4, OOPSLA (2020)
Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference remains the primary roadblock for applying PPLs in practice. Inference
Externí odkaz:
http://arxiv.org/abs/2005.09089
A representation invariant is a property that holds of all values of abstract type produced by a module. Representation invariants play important roles in software engineering and program verification. In this paper, we develop a counterexample-drive
Externí odkaz:
http://arxiv.org/abs/2003.12106