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Correctness of Sequential Monte Carlo Inference for probabilistic programming Languages 1

30th European Symposium on programming (ESOP) Held as Part of the 24th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Lunden, Daniel Borgstrom, Johannes Broman, David KTH Royal Inst Technol Digital Futures Stockholm Sweden KTH Royal Inst Technol EECS Stockholm Sweden Uppsala Univ Uppsala Sweden

ISBN: (数字)9783030720193

ISBN: (纸本)9783030720193;9783030720186

probabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing research on such algorithms mainly concerns their implementation and efficiency, rather than the correctness of the algorithms themselves when applied in the context of expressive PPLs. To remedy this, we give a correctness proof for SMC methods in the context of an expressive PPL calculus, representative of popular PPLs such as WebPPL, Anglican, and Birch. Previous work have studied correctness of MCMC using an operational semantics, and correctness of SMC and MCMC in a denotational setting without term recursion. However, for SMC inference-one of the most commonly used algorithms in PPLs as of today-no formal correctness proof exists in an operational setting. In particular, an open question is if the resample locations in a probabilistic program affects the correctness of SMC. We solve this fundamental problem, and make four novel contributions: (i) we extend an untyped PPL lambda calculus and operational semantics to include explicit resample terms, expressing synchronization points in SMC inference;(ii) we prove, for the first time, that subject to mild restrictions, any placement of the explicit resample terms is valid for a generic form of SMC inference;(iii) as a result of (ii), our calculus benefits from classic results from the SMC literature: a law of large numbers and an unbiased estimate of the model evidence;and (iv) we formalize the bootstrap particle filter for the calculus and discuss how our results can be further extended to other SMC algorithms.

关键词： probabilistic programming Sequential Monte Carlo Operational Semantics Functional programming Measure Theory

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Guaranteed Bounds for Posterior Inference in Universal probabilistic programming 2022

Guaranteed Bounds for Posterior Inference in Universal Proba...

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43rd ACM SIGPLAN International Conference on programming Language Design and Implementation (PLDI)

作者： Beutner, Raven Ong, C-H Luke Zaiser, Fabian CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Univ Oxford Oxford England

ISBN: (纸本)9781450392655

We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, higher-order probabilistic programming language with continuous distributions. Taking the form of (super-/subadditive) measures, these lower/upper bounds are non-stochastic and provably correct: using the semantics, we prove that the actual posterior of a given program is sandwiched between the lower and upper bounds (soundness);moreover, the bounds converge to the posterior (completeness). As a practical and sound approximation, we introduce a weight-aware interval type system, which automatically infers interval bounds on not just the return value but also the weight of program executions, simultaneously. We have built a tool implementation, called GuBPI, which automatically computes these posterior lower/upper bounds. Our evaluation on examples from the literature shows that the bounds are useful, and can even be used to recognise wrong outputs from stochastic posterior inference procedures.

关键词： probabilistic programming Bayesian inference verification abstract interpretation operational semantics interval arithmetic type system symbolic execution

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Reactive probabilistic programming 2020

Reactive Probabilistic Programming

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41st ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI)

作者： Baudart, Guillaume Mandel, Louis Atkinson, Eric Sherman, Benjamin Pouzet, Marc Carbin, Michael IBM Res MIT IBM Watson AI Lab Armonk NY 10504 USA MIT Cambridge MA 02139 USA PSL Res Univ Ecole Normale Super Paris France

ISBN: (纸本)9781450376136

Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or SCADE used routinely for implementing safety critical control software, e.g., fly-by-wire and engine control in planes. However, to date these languages have had limited modern support for modeling uncertainty - probabilistic aspects of the software's environment or behavior - even though modeling uncertainty is a primary activity when designing a control system. In this paper we present ProbZelus the first synchronous probabilistic programming language. ProbZelus conservatively provides the facilities of a synchronous language to write control software, with probabilistic constructs to model uncertainties and perform inference-in-the-loop. We present the design and implementation of the language. We propose a measure-theoretic semantics of probabilistic stream functions and a simple type discipline to separate deterministic and probabilistic expressions. We demonstrate a semantics-preserving compilation into a first-order functional language that lends itself to a simple presentation of inference algorithms for streaming models. We also redesign the delayed sampling inference algorithm to provide efficient streaming inference. Together with an evaluation on several reactive applications, our results demonstrate that ProbZelus enables the design of reactive probabilistic applications and efficient, bounded memory inference.

关键词： probabilistic programming Reactive programming Streaming inference Semantics Compilation

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Real-Time GA-Based probabilistic programming in Application to Robot Control 1

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9th International Conference on Artificial General Intelligence (AGI) Held as Part of Joint Multi-Conference on Human-Level Intelligence (HLAI)

作者： Potapov, Alexey Rodionov, Sergey Potapova, Vita ITMO Univ St Petersburg Russia St Petersburg State Univ St Petersburg Russia AIDEUS St Petersburg Russia Aix Marseille Univ CNRS LAM UMR 7326 F-13388 Marseille France

ISBN: (数字)9783319416496

ISBN: (纸本)9783319416496;9783319416489

Possibility to solve the problem of planning and plan recovery for robots using probabilistic programming with optimization queries, which is being developed as a framework for AGI and cognitive architectures, is considered. Planning can be done directly by introducing a generative model for plans and optimizing an objective function calculated via plan simulation. Plan recovery is achieved almost without modifying optimization queries. These queries are simply executed in parallel with plan execution by a robot meaning that they continuously optimize dynamically varying objective functions tracking their optima. Experiments with the NAO robot showed that replanning can be naturally done within this approach without developing special plan recovery methods.

关键词： probabilistic programming Optimization queries Genetic algorithms Robot planning Replanning

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Automatic Alignment in Higher-Order probabilistic programming Languages 32nd

Automatic Alignment in Higher-Order Probabilistic Programmin...

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32nd European Symposium on programming (ESOP) Held as Part of the 26th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Lunden, Daniel Caylak, Gizem Ronquist, Fredrik Broman, David KTH Royal Inst Technol EECS & Digital Futures Stockholm Sweden Swedish Museum Nat Hist Dept Bioinformat & Genet Stockholm Sweden Stockholm Univ Dept Zool Stockholm Sweden

ISBN: (纸本)9783031300431;9783031300448

probabilistic programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints-relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run-they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC.

关键词： probabilistic programming Operational semantics Static analysis

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Vate: Runtime Adaptable probabilistic programming for Java 1

Vate: Runtime Adaptable Probabilistic Programming for Java

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1st Workshop on Machine Learning and Systems (EuroMLSys)

作者： Goodman, Daniel Pocock, Adam Peck, Jason Steele, Guy Oracle Labs Redwood City CA 94065 USA

ISBN: (纸本)9781450382984

Inspired by earlier work on Augur, Vate is a probabilistic programming language for the construction of JVM based probabilistic models with an Object-Oriented interface. As a compiled language it is able to examine the dependency graph of the model to produce optimised code that can be dynamically targeted to different platforms. Using Gibbs Sampling, Metropolis-Hastings and variable marginalisation it can handle a range of model types and is able to efficiently infer values, estimate probabilities, and execute models.

关键词： probabilistic programming Java Augur MCMC Parallel programming

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Stan: A probabilistic programming Language for Bayesian Inference and Optimization

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JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS 2015年第5期40卷 530-543页

作者： Gelman, Andrew Lee, Daniel Guo, Jiqiang Columbia Univ Stat & Polit Sci New York NY 10027 USA Columbia Univ New York NY 10027 USA

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers' perspectives and illustrate with a simple but nontrivial nonlinear regression example.

关键词： Bayesian inference hierarchical models probabilistic programming statistical computing

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Compiling Stan to Generative probabilistic Languages and Extension to Deep probabilistic programming 2021

Compiling Stan to Generative Probabilistic Languages and Ext...

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42nd ACM SIGPLAN International Conference on programming Language Design and Implementation (PLDI)

作者： Baudart, Guillaume Burroni, Javier Hirzel, Martin Mandel, Louis Shinnar, Avraham PSL Univ Ecole Normale Super INRIA Paris Paris France UMass Amherst Amherst MA USA IBM Res MIT IBM Watson AI Lab Armonk NY USA

ISBN: (纸本)9781450383912

Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro. Experimental results show that the NumPyro backend yields a 2.3x speedup compared to Stan in geometric mean over 26 benchmarks. Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language.

关键词： probabilistic programming Semantics Stan Pyro

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Democratizing AI for Condition-Based Maintenance Leveraging probabilistic programming for Symbolic Reasoning

Democratizing AI for Condition-Based Maintenance Leveraging ...

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69th Annual Reliability and Maintainability Symposium (RAMS)

作者： Lu, Kenneth Cvijic, Sanja Dewhurst, David Gorman, Joe Hyland, Rob Templin, James Charles River Analyt 625 Mt Auburn St Cambridge MA 02138 USA Army Futures Command 701 Brazos St Austin TX 78701 USA

ISBN: (纸本)9781665460538

Advances in AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes;however, AI/ML solutions themselves inevitably become a maintenance liability, wherein the end users must repeatedly work with a data scientist to update the AI/ML application. In this paper, we present novel research that alleviates this problem by enabling non-AI/ML experts to create and maintain AI/ML applications with minimal guidance from a data scientist. We describe this research as the "Democratization" of AI/ML and accomplish this by leveraging cutting edge techniques in knowledge capture and probabilistic programming as a way to represent higher level symbolic reasoning and delegating sub-symbolic reasoning to traditional data driven approaches.

关键词： artificial intelligence condition-based maintenance probabilistic programming symbolic reasoning

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Tabular: A Schema-Driven probabilistic programming Language 14

Tabular: A Schema-Driven Probabilistic Programming Language

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

作者： Gordon, Andrew D. Graepel, Thore Rolland, Nicolas Russo, Claudio Borgstroem, Johannes Guiver, John Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland Uppsala Univ Uppsala Sweden

ISBN: (纸本)9781450325448

We propose a new kind of probabilistic programming language for machine learning. We write programs simply by annotating existing relational schemas with probabilistic model expressions. We describe a detailed design of our language, Tabular, complete with formal semantics and type system. A rich series of examples illustrates the expressiveness of Tabular. We report an implementation, and show evidence of the succinctness of our notation relative to current best practice. Finally, we describe and verify a transformation of Tabular schemas so as to predict missing values in a concrete database. The ability to query for missing values provides a uniform interface to a wide variety of tasks, including classification, clustering, recommendation, and ranking.

关键词： Bayesian reasoning machine learning model-learner pattern probabilistic programming relational data

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