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Related papers: Probabilistic Program Equivalence for NetKAT

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We consider online similarity prediction problems over networked data. We begin by relating this task to the more standard class prediction problem, showing that, given an arbitrary algorithm for class prediction, we can construct an…

Machine Learning · Computer Science 2013-03-18 Claudio Gentile , Mark Herbster , Stephen Pasteris

This paper considers the computational hardness of computing expected outcomes and deciding almost-sure termination of probabilistic programs. We show that deciding almost-sure termination and deciding whether the expected outcome of a…

Logic in Computer Science · Computer Science 2014-10-28 Benjamin Lucien Kaminski , Joost-Pieter Katoen

When the inverse of an algorithm is well-defined -- that is, when its output can be deterministically transformed into the input producing it -- we say that the algorithm is invertible. While one can describe an invertible algorithm using a…

Programming Languages · Computer Science 2022-12-07 Joachim Tilsted Kristensen , Robin Kaarsgaard , Michael Kirkedal Thomsen

Opacity is a generic security property, that has been defined on (non probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an…

Cryptography and Security · Computer Science 2014-09-02 Béatrice Bérard , Krishnendu Chatterjee , Nathalie Sznajder

We consider qualitative and quantitative verification problems for infinite-state Markov chains. We call a Markov chain decisive w.r.t. a given set of target states F if it almost certainly eventually reaches either F or a state from which…

Logic in Computer Science · Computer Science 2015-07-01 Parosh Aziz Abdulla , Noomene Ben Henda , Richard Mayr

Modern distributed systems include a class of applications in which non-functional requirements are important. In particular, these applications include multimedia facilities where real time constraints are crucial to their correct…

Multimedia · Computer Science 2007-05-23 Jeremy Bryans , Howard Bowman , John Derrick

Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities. These compositions are…

This paper proposes a notion of branching bisimilarity for non-deterministic probabilistic processes. In order to characterize the corresponding notion of rooted branching probabilistic bisimilarity, an equational theory is proposed for a…

Logic in Computer Science · Computer Science 2025-02-11 Rob van Glabbeek , Jan Friso Groote , Erik de Vink

Consider longitudinal networks whose edges turn on and off according to a discrete-time Markov chain with exponential-family transition probabilities. We characterize when their joint distributions are also exponential families with the…

Methodology · Statistics 2024-03-12 William K. Schwartz , Sonja Petrović , Hemanshu Kaul

We present an efficient algorithm for checking language equivalence of states in top-down deterministic finite tree automata (DFTAs). Unlike string automata, tree automata operate over hierarchical structures, posing unique challenges for…

Formal Languages and Automata Theory · Computer Science 2025-04-08 Zhibo Deng , Vladimir A. Zakharov

The EM-algorithm is a general procedure to get maximum likelihood estimates if part of the observations on the variables of a network are missing. In this paper a stochastic version of the algorithm is adapted to probabilistic neural…

Artificial Intelligence · Computer Science 2013-03-26 Gerhard Paass

We use Hidden Markov Models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or "implements" relation that compares two programs with respect to their information…

Cryptography and Security · Computer Science 2019-02-20 Annabelle McIver , Larissa Meinicke , Carroll Morgan

The value 1 problem is a decision problem for probabilistic automata over finite words: given a probabilistic automaton A, are there words accepted by A with probability arbitrarily close to 1? This problem was proved undecidable recently.…

Formal Languages and Automata Theory · Computer Science 2012-01-27 Nathanaël Fijalkow , Hugo Gimbert , Youssouf Oualhadj

In order to represent the preferences of a group of individuals, we introduce Probabilistic CP-nets (PCP-nets). PCP-nets provide a compact language for representing probability distributions over preference orderings. We argue that they are…

Artificial Intelligence · Computer Science 2013-09-27 Damien Bigot , Bruno Zanuttini , Helene Fargier , Jerome Mengin

Chance constrained program is computationally intractable due to the existence of chance constraints, which are randomly disturbed and should be satisfied with a probability. This paper proposes a two-layer randomized algorithm to address…

Optimization and Control · Mathematics 2019-11-11 Xun Shen , Jiancang Zhuang , Xingguo Zhang

We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy to parallelize. It applies to…

Machine Learning · Statistics 2015-07-10 Frank Wood , Jan Willem van de Meent , Vikash Mansinghka

Reliable and low latency multicast communication is important for future vehicular communication. Sparse random linear network coding approach used to ensure the reliability of multicast communication has been widely investigated. A…

Information Theory · Computer Science 2020-10-13 Wenlin Chen , Fang Lu , Yan Dong

We study the problem of completely automatically verifying uninterpreted programs---programs that work over arbitrary data models that provide an interpretation for the constants, functions and relations the program uses. The verification…

Programming Languages · Computer Science 2020-08-27 Umang Mathur , P. Madhusudan , Mahesh Viswanathan

Testing whether a probability distribution is compatible with a given Bayesian network is a fundamental task in the field of causal inference, where Bayesian networks model causal relations. Here we consider the class of causal structures…

Machine Learning · Statistics 2020-09-04 Aditya Kela , Kai von Prillwitz , Johan Aberg , Rafael Chaves , David Gross

In many branches of engineering, Banach contraction mapping theorem is employed to establish the convergence of certain deterministic algorithms. Randomized versions of these algorithms have been developed that have proved useful in…

Probability · Mathematics 2023-09-25 Abhishek Gupta , Rahul Jain , Peter Glynn