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检索条件"主题词=Probabilistic Programming"

共 321 条记录，以下是221-230 订阅

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A Denotational Semantics for Low-Level probabilistic Programs with Nondeterminism 35th

A Denotational Semantics for Low-Level Probabilistic Program...

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35th Conference on the Mathematical Foundations of programming Semantics

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

probabilistic programming is an increasingly popular formalism for modeling randomness and uncertainty. Designing semantic models for probabilistic programs has been extensively studied, but is technically challenging. Particular complications arise when trying to account for (i) unstructured control-flow, a natural feature in low-level imperative programs;(ii) general recursion, an extensively used programming paradigm;and (iii) nondeterminism, which is often used to represent adversarial actions in probabilistic models, and to support refinement-based development. This paper presents a denotational-semantics framework that supports the three features mentioned above, while allowing nondeterminism to be handled in different ways. To support both probabilistic choice and nondeterministic choice, the semantics is given over control-flow hyper-graphs. The semantics follows an algebraic approach: it can be instantiated in different ways as long as certain algebraic properties hold. In particular, the semantics can be instantiated to support nondeterminism among either program states or state transformers. We develop a new formalization of nondeterminism based on powerdomains over sub-probability kernels. Semantic objects in the powerdomain enjoy a notion we call generalized convexity, which is a generalization of convexity. As an application, the paper sketches an algebraic framework for static analysis of probabilistic programs, which has been proposed in a companion paper.

关键词： probabilistic programming denotational semantics control-flow hyper-graphs nondeterminism powerdomains

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probabilistic Models for Assured Position, Navigation, and Timing

Probabilistic Models for Assured Position, Navigation, and T...

引用

Conference on Autonomous Systems - Sensors, Vehicles, Security, and the Internet of Everything

作者： Molina-Markham, Andres D. MITRE Corp 202 Burlington Rd Bedford MA 01730 USA

ISBN: (数字)9781510617988

ISBN: (纸本)9781510617988

Position, navigation, and timing (PNT) user equipment produces position, velocity, and time (PVT) estimates by combining measurements from multiple Global Navigation Satellite Systems (GNSS) and from additional sensors. PVT estimates are computed using linear estimators or Bayesian filters. However, because linear estimators and Bayesian filters are susceptible to adversarial manipulation, it is challenging to assess the trust of PVT estimates that rely on these approaches. We investigate the suitability of open-universe probabilistic models OUPMs-introduced by Milch and Russell-as a foundation to design PVT assurance metrics and adaptive PVT estimators. These estimators output PVT information together with trust assessments of PVT inputs and outputs. OUPMs model structural uncertainty (object uncertainty and relational uncertainty) necessary to measure assurance when the availability of sensors and the absence of adversaries cannot be guaranteed. We describe the challenges of designing PVT assurance metrics using traditional methods, and we illustrate how OUPMs-represented as probabilistic programs-allow us to address these challenges. In particular, we provide concrete examples of how to combine multiple sources of information to compute assurance assessments using the Texas Spoofing Test Battery. Furthermore, we demonstrate how to leverage PVT assurance metrics to design adaptive PVT estimators designed to operate through attacks.

关键词： probabilistic programming assurance models position navigation and timing

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Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence 8

Less (Data) Is More: Why Small Data Holds the Key to the Fut...

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8th International Conference on Data Science, Technology and Applications (DATA)

作者： Greco, Ciro Polonioli, Andrea Tagliabue, Jacopo Tooso Labs San Francisco CA 94102 USA

ISBN: (纸本)9789897583773

The claims that big data holds the key to enterprise successes and that Artificial Intelligence (AI) is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry. However, while these claims may indeed capture some truth, they have also been massively oversold, or so we contend here. The goal of this paper is two-fold. First, we provide a qualified defence of the value of less data within the context of AI. This is done by carefully reviewing two distinct problems for big data driven AI, namely a) the limited track record of Deep Learning (DL) in key areas such as Natural Language Processing (NLP), b) the regulatory and business significance of being able to learn from few data points. Second, we briefly sketch what we refer to as a case of "A.I. with humans and for humans", namely an AI paradigm whereby the systems we build are privacy-oriented and focused on human-machine collaboration, not competition. Combining our claims above, we conclude that when seen through the lens of cognitively inspired A.I., the bright future of the discipline is about less data, not more, and more humans, not fewer.

关键词： Artificial Intelligence Big Data probabilistic programming Concept Learning Machine Learning

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Distribution-Aware Sampling of Answer Sets 12th

Distribution-Aware Sampling of Answer Sets

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12th International Conference on Scalable Uncertainty Management (SUM)

作者： Nickles, Matthias Natl Univ Ireland Sch Engn & Informat Galway Ireland

ISBN: (纸本)9783030004613;9783030004606

Distribution-aware answer set sampling has a wide range of potential applications, for example in the area of probabilistic logic programming or for the computation of approximate solutions of combinatorial or search problems under uncertainty. This paper introduces algorithms for the sampling of answer sets under given probabilistic constraints. Our approaches allow for the specification of probability distributions over stable models using probabilistically weighted facts and rules as constraints for an approximate sampling task with specifiable accuracy. At this, we do not impose any independence requirements on random variables. An experimental evaluation investigates the performance characteristics of the presented algorithms.

关键词： Nonmonotonic probabilistic Logic programming Answer Set programming (ASP) Relational Artificial Intelligence probabilistic programming

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Bounded Expectations: Resource Analysis for probabilistic Programs 2018

Bounded Expectations: Resource Analysis for Probabilistic Pr...

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

作者： Van Chan Ngo Carbonneaux, Quentin Hoffmann, Jan Carnegie Mellon Univ Pittsburgh PA 15213 USA Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781450356985

This paper presents a new static analysis for deriving upper bounds on the expected resource consumption of probabilistic programs. The analysis is fully automatic and derives symbolic bounds that are multivariate polynomials in the inputs. The new technique combines manual state-of-the-art reasoning techniques for probabilistic programs with an effective method for automatic resource-bound analysis of deterministic programs. It can be seen as both, an extension of automatic amortized resource analysis (AARA) to probabilistic programs and an automation of manual reasoning for probabilistic programs that is based on weakest preconditions. An advantage of the technique is that it combines the clarity and compositionality of a weakest-precondition calculus with the efficient automation of AARA. As a result, bound inference can be reduced to off-the-shelf LP solving in many cases and automatically-derived bounds can be interactively extended with standard program logics if the automation fails. Building on existing work, the soundness of the analysis is proved with respect to an operational semantics that is based on Markov decision processes. The effectiveness of the technique is demonstrated with a prototype implementation that is used to automatically analyze 39 challenging probabilistic programs and randomized algorithms. Experiments indicate that the derived constant factors in the bounds are very precise and even optimal for some programs.

关键词： probabilistic programming resource bound analysis static analysis

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Bayonet: probabilistic Inference for Networks 2018

Bayonet: Probabilistic Inference for Networks

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

作者： Gehr, Timon Misailovic, Sasa Tsankov, Petar Vanbever, Laurent Wiesmann, Pascal Vechev, Martin Swiss Fed Inst Technol Zurich Switzerland UIUC Champaign IL USA

ISBN: (纸本)9781450356985

Network operators often need to ensure that important probabilistic properties are met, such as that the probability of network congestion is below a certain threshold. Ensuring such properties is challenging and requires both a suitable language for probabilistic networks and an automated procedure for answering probabilistic inference queries. We present BAYONET, a novel approach that consists of: (i) a probabilistic network programming language and (ii) a system that performs probabilistic inference on BAYONET programs. The key insight behind BAYONET is to phrase the problem of probabilistic network reasoning as inference in existing probabilistic languages. As a result, BAYONET directly leverages existing probabilistic inference systems and offers a flexible and expressive interface to operators. We present a detailed evaluation of BAYONET on common network scenarios, such as network congestion, reliability of packet delivery, and others. Our results indicate that BAYONET can express such practical scenarios and answer queries for realistic topology sizes (with up to 30 nodes).

关键词： probabilistic programming Computer Networks

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Sound probabilistic Inference via Guide Types 2021

Sound Probabilistic Inference via Guide Types

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42nd ACM SIGPLAN International 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

ISBN: (纸本)9781450383912

probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs to improve inference performance. For Bayesian inference to be sound, guide programs must be compatible with model programs. One pervasive but challenging condition for model-guide compatibility is absolute continuity, which requires that the model and guide programs define probability distributions with the same support. This paper presents a new probabilistic programming language that guarantees absolute continuity, and features general programming constructs, such as branching and recursion. Model and guide programs are implemented as coroutines that communicate with each other to synchronize the set of random variables they sample during their execution. Novel guide types describe and enforce communication protocols between coroutines. If the model and guide are well-typed using the same protocol, then they are guaranteed to enjoy absolute continuity. An efficient algorithm infers guide types from code so that users do not have to specify the types. The new programming language is evaluated with an implementation that includes the type-inference algorithm and a prototype compiler that targets Pyro. Experiments show that our language is capable of expressing a variety of probabilistic models with nontrivial control flow and recursion, and that the coroutine-based computation does not introduce significant overhead in actual Bayesian inference.

关键词： probabilistic programming Bayesian inference type systems coroutines

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Warped Input Gaussian Processes for Time Series Forecasting 1

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5th International Symposium on Cyber Security Cryptography and Machine Learning (CSCML)

作者： Vinokur, Igor Tolpin, David Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (数字)9783030780869

ISBN: (纸本)9783030780869;9783030780852

Time series forecasting plays a vital role in system monitoring and novelty detection. However, commonly used forecasting methods are not suited for handling non-stationarity, while existing methods for forecasting in non-stationary time series are often complex to implement and involve expensive computations. We introduce a Gaussian process-based model for handling of non-stationarity. The warping is achieved non-parametrically, through imposing a prior on the relative change of distance between subsequent observation inputs. The model allows the use of general gradient optimization algorithms for training and incurs only a small computational overhead on training and prediction. The model finds its applications in forecasting in non-stationary time series with either gradually varying volatility, presence of change points, or a combination thereof. We implement the model in a probabilistic programming framework, evaluate on synthetic and real-world time series data comparing against both broadly used baselines and known state-of-the-art approaches and show that the model exhibits state-of-the-art forecasting performance at a lower implementation and computation cost, enabling efficient applications in diverse fields of system monitoring and novelty detection..

关键词： Time series probabilistic programming Non-stationarity Gaussian processes

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Incremental Inference for probabilistic Programs 2018

Incremental Inference for Probabilistic Programs

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

作者： Cusumano-Towner, Marco Bichsel, Benjamin Gehr, Timon Vechev, Martin Mansinghka, Vikash K. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450356985

We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program P into samples for a program Q, thereby avoiding the expensive sampling computation for program Q. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from P into samples of Q, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by Q, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program Q from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related P and Q are.

关键词： probabilistic programming sequential Monte Carlo incremental computation

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Output-Sensitive Adaptive Metropolis-Hastings for probabilistic Programs

Output-Sensitive Adaptive Metropolis-Hastings for Probabilis...

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

作者： Tolpin, David de Meent, Jan-Willem van Paige, Brooks Wood, Frank Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

ISBN: (纸本)9783319235257;9783319235240

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). This algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct equilibrium distribution and compare convergence of AdLMH to that of LMH on several test problems to highlight different aspects of the adaptation scheme. We observe consistent improvement in convergence on the test problems.

关键词： probabilistic programming Adaptive MCMC

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