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检索条件"主题词=Probabilistic Programming"

共 321 条记录，以下是251-260 订阅

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probabilistic Relational Verification for Cryptographic Implementations 14

Probabilistic Relational Verification for Cryptographic Impl...

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41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of programming Languages (POPL)

作者： Barthe, Gilles Fournet, Cedric Gregoire, Benjamin Strub, Pierre-Yves Swamy, Nikhil Zanella-Beguelin, Santiago IMDEA Software Inst Madrid Spain INRIA Paris France

ISBN: (纸本)9781450325448

Relational program logics have been used for mechanizing formal proofs of various cryptographic constructions. With an eye towards scaling these successes towards end-to-end security proofs for implementations of distributed systems, we present RF*, a relational extension of F*, a general-purpose higher-order stateful programming language with a verification system based on refinement types. The distinguishing feature of RF* is a relational Hoare logic for a higher-order, stateful, probabilistic language. Through careful language design, we adapt the F* typechecker to generate both classic and relational verification conditions, and to automatically discharge their proofs using an SMT solver. Thus, we are able to benefit from the existing features of F*, including its abstraction facilities for modular reasoning about program fragments. We evaluate RF* experimentally by programming a series of cryptographic constructions and protocols, and by verifying their security properties, ranging from information flow to unlinkability, integrity, and privacy. Moreover, we validate the design of RF* by formalizing in Coq a core probabilistic lambda-calculus and a relational refinement type system and proving the soundness of the latter against a denotational semantics of the probabilistic lambda-calculus.

关键词： program logics probabilistic programming

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Bayesian Hierarchical Modelling for Tailoring Metric Thresholds 15

Bayesian Hierarchical Modelling for Tailoring Metric Thresho...

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ACM/IEEE 15th International Conference on Mining Software Repositories (MSR)

作者： Ernst, Neil A. Univ Victoria Dept Comp Sci Victoria BC Canada

ISBN: (纸本)9781450357166

Software is highly contextual. While there are cross-cutting 'global' lessons, individual software projects exhibit many 'local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research challenge is to construct locally accurate prediction models that are informed by global characteristics and data volumes. Previous work has tackled this problem using clustering and transfer learning approaches, which identify locally similar characteristics. This paper applies a simpler approach known as Bayesian hierarchical modeling. We show that hierarchical modeling supports cross-project comparisons, while preserving local context. To demonstrate the approach, we conduct a conceptual replication of an existing study on setting software metrics thresholds. Our emerging results show our hierarchical model reduces model prediction error compared to a global approach by up to 50%.

关键词： hierarchical models metrics thresholds probabilistic programming

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Incremental Precision-Preserving Symbolic Inference for probabilistic Programs 2019

Incremental Precision-Preserving Symbolic Inference for Prob...

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40th ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI) part of ACM's Federated Computing Research Conference (FCRC)

作者： Zhang, Jieyuan Xue, Jingling UNSW Sydney Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781450367127

We present ISymb, an incremental symbolic inference framework for probabilistic programs in situations when some loop-manipulated array data, upon which their probabilistic models are conditioned, undergoes small changes. To tackle the path explosion challenge, ISymb is intra-procedurally path-sensitive except that it conducts a "meet-over-all-paths" analysis within an iteration of a loop (conditioned on some observed array data). By recomputing only the probability distributions for the paths affected, ISymb avoids expensive symbolic inference from scratch while also being precision-preserving. Our evaluation with a set of existing benchmarks shows that ISymb can lead to orders of magnitude performance improvements compared to its non-incremental counterpart (under small changes in observed array data).

关键词： probabilistic programming Symbolic Inference Incremental Computation Static analysis

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Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo

Controlling Procedural Modeling Programs with Stochastically...

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ACM SIGGRAPH Conference

作者： Ritchie, Daniel Mildenhall, Ben Goodman, Noah D. Hanrahan, Pat Stanford Univ Stanford CA 94305 USA

We present a method for controlling the output of procedural modeling programs using Sequential Monte Carlo (SMC). Previous probabilistic methods for controlling procedural models use Markov Chain Monte Carlo (MCMC), which receives control feedback only for completely-generated models. In contrast, SMC receives feedback incrementally on incomplete models, allowing it to reallocate computational resources and converge quickly. To handle the many possible sequentializations of a structured, recursive procedural modeling program, we develop and prove the correctness of a new SMC variant, Stochastically-Ordered Sequential Monte Carlo (SOSMC). We implement SOSMC for general-purpose programs using a new programming primitive: the stochastic future. Finally, we show that SOSMC reliably generates high-quality outputs for a variety of programs and control scoring functions. For small computational budgets, SOSMC's outputs often score nearly twice as high as those of MCMC or normal SMC.

关键词： Procedural Modeling Directable Randomness probabilistic programming Sequential Monte Carlo

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A Higher-Order Language for Markov Kernels and Linear Operators 1

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26th International Conference on Foundations of Software Science and Computational Structures (FOSSACS)

作者： de Amorim, Pedro H. Azevedo Cornell Univ Ithaca NY 14850 USA

ISBN: (数字)9783031308291

ISBN: (纸本)9783031308284;9783031308291

Much work has been done to give semantics to probabilistic programming languages. In recent years, most of the semantics used to reason about probabilistic programs fall in two categories: semantics based on Markov kernels and semantics based on linear operators. Both styles of semantics have found numerous applications in reasoning about probabilistic programs, but they each have their strengths and weaknesses. Though it is believed that there is a connection between them there are no languages that can handle both styles of programming. In this work we address these questions by defining a two-level calculus and its categorical semantics which makes it possible to program with both kinds of semantics. From the logical side of things we see this language as an alternative resource interpretation of linear logic, where the resource being kept track of is sampling instead of variable use.

关键词： Linear Logic probabilistic programming Categorical Semantics

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SixthSense: Debugging Convergence Problems in probabilistic Programs via Program Representation Learning 25th

SixthSense: Debugging Convergence Problems in Probabilistic ...

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25th International Conference on Fundamental Approaches to Software Engineering (FASE) Held as Part of the Annual European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Dutta, Saikat Huang, Zixin Misailovic, Sasa Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9783030994297;9783030994280

probabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during inference and debugging are left entirely to the developers and typically require significant statistical expertise. A common class of problems when writing probabilistic programs is the lack of convergence of the probabilistic programs to their posterior distributions. We present SixthSense, a novel approach for predicting probabilistic program convergence ahead of run and its application to debugging convergence problems in probabilistic programs. SixthSense's training algorithm learns a classifier that can predict whether a previously unseen probabilistic program will converge. It encodes the syntax of a probabilistic program as motifs - fragments of the syntactic program paths. The decisions of the classifier are interpretable and can be used to suggest the program features that contributed significantly to program convergence or non-convergence. We also present an algorithm for augmenting a set of training probabilistic programs that uses guided mutation. We evaluated SixthSense on a broad range of widely used probabilistic programs. Our results show that SixthSense features are effective in predicting convergence of programs for given inference algorithms. SixthSense obtained Accuracy of over 78% for predicting convergence, substantially above the state-of-the-art techniques for predicting program properties Code2Vec and Code2Seq. We show the ability of SixthSense to guide the debugging of convergence problems, which pinpoints the causes of non-convergence significantly better by Stan's built-in warnings.

关键词： probabilistic programming Debugging Machine Learning

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Mastering Uncertainty in Performance Estimations of Configurable Software Systems 35

Mastering Uncertainty in Performance Estimations of Configur...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Dorn, Johannes Apel, Sven Siegmund, Norbert Univ Leipzig Leipzig Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9781450367684

Understanding the influence of configuration options on performance is key for finding optimal system configurations, system understanding, and performance debugging. In prior research, a number of performance-influence modeling approaches have been proposed, which model a configuration option's influence and a configuration's performance as a scalar value. However, these point estimates falsely imply a certainty regarding an option's influence that neglects several sources of uncertainty within the assessment process, such as (1) measurement bias, (2) model representation and learning process, and (3) incomplete data. This leads to the situation that different approaches and even different learning runs assign different scalar performance values to options and interactions among them. The true influence is uncertain, though. There is no way to quantify this uncertainty with state-of-the-art performance modeling approaches. We propose a novel approach, P4, based on probabilistic programming that explicitly models uncertainty for option influences and consequently provides a confidence interval for each prediction of a configuration's performance alongside a scalar. This way, we can explain, for the first time, why predictions may cause errors and which option's influences may be unreliable. An evaluation on 12 real-world subject systems shows that P4's accuracy is in line with the state of the art while providing reliable confidence intervals, in addition to scalar predictions.

关键词： probabilistic programming Performance-Influence Modeling Configurable Software Systems P4

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PMAF: An Algebraic Framework for Static Analysis of probabilistic Programs 2018

PMAF: An Algebraic Framework for Static Analysis of Probabil...

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39th ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI)

作者： Wang, Di Hoffmann, Jan Reps, Thomas Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Wisconsin Madison WI 53706 USA GrammaTech Inc Ithaca NY USA

ISBN: (纸本)9781450356985

Automatically establishing that a probabilistic program satisfies some property phi is a challenging problem. While a sampling-based approach-which involves running the program repeatedly-can suggest that phi holds, to establish that the program satisfies phi, analysis techniques must be used. Despite recent successes, probabilistic static analyses are still more difficult to design and implement than their deterministic counterparts. This paper presents a framework, called PMAF, for designing, implementing, and proving the correctness of static analyses of probabilistic programs with challenging features such as recursion, unstructured control-flow, divergence, nondeterminism, and continuous distributions. PMAF introduces pre-Markov algebras to factor out common parts of different analyses. To perform interprocedural analysis and to create procedure summaries, PMAF extends ideas from non-probabilistic interprocedural dataflow analysis to the probabilistic setting. One novelty is that PMAF is based on a semantics formulated in terms of a control-flow hypergraph for each procedure, rather than a standard control-flow graph. To evaluate its effectiveness, PMAF has been used to reformulate and implement existing intraprocedural analyses for Bayesian-inference and the Markov decision problem, by creating corresponding interprocedural analyses. Additionally, PMAF has been used to implement a new interprocedural linear expectation-invariant analysis. Experiments with benchmark programs for the three analyses demonstrate that the approach is practical.

关键词： Program analysis probabilistic programming expectation invariant pre-Markov algebra

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Temperature Driven Bayesian probabilistic Modelling of Electricity Demand, Capacity, and Adequacy

Temperature Driven Bayesian Probabilistic Modelling of Elect...

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International Conference on probabilistic Methods Applied to Power Systems (PMAPS)

作者： Ahmed, Elyas Sohm, Daniel Toronto Canada

ISBN: (纸本)9781728128221

The declining costs for various distributed energy resources such as solar and energy storage is driving an increase in the penetration level of these resources at the grid's edge. The electricity market operator must account for these changes to effectively plan the system's demand, supply, and adequacy for various scenarios. This paper proposes a simplified methodology to create a probabilistic model of demand and supply which can be used to model resource adequacy as a function of temperature. This adequacy model is then translated to describe adequacy by duration of need. This description can then inform the duration of service needed from limited energy storage resources to reduce the probability of load being unserved. We first use a Bayesian additive model to infer the relationship between demand and available capacity as function of temperature. We then calculate the probability for when demand will be greater than supply for each unit increment of temperature. This probability can be described as a binomial random variable of demand being greater than supply for that hour. Finally, we estimate the duration of need by approximating the sum of binomial random variables for the day. With this methodology, one can rapidly simulate various supply mixes by fuel type to understand its effects on the final duration of need.

关键词： probabilistic Resource Adequacy Modelling Demand Modelling Modelling Available Capacity Reliability Bayesian Learning probabilistic programming

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SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract) 19th

SynthLog: A Language for Synthesising Inductive Data Models ...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Dauxais, Yann Gautrais, Clement Dries, Anton Jain, Arcchit Kolb, Samuel Kumar, Mohit Teso, Stefano Van Wolputte, Elia Verbruggen, Gust De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Celestijnenlaan 200A Leuven Belgium

ISBN: (纸本)9783030438234;9783030438227

We introduce SynthLog, an extension of the probabilistic logic programming language ProbLog, for synthesising inductive data models. Inductive data models integrate data with predictive and descriptive models, in a way that is reminiscent of inductive databases. SynthLog provides primitives for learning and manipulating inductive data models, it supports data wrangling, learning predictive models and constraints, and probabilistic and constraint reasoning. It is used as the back-end of the automated data scientist approach that is being developed in the SYNTH project. An overview of the SynthLog philosophy and language as well as a non trivial example of its use, is given in this paper.

关键词： Automated data science Inductive databases probabilistic programming

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