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Trace Types and Denotational Semantics for Sound Programmable Inference in probabilistic Languages

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第POPL期4卷 1–32页

作者： Lew, Alexander K. Cusumano-Towner, Marco F. Sherman, Benjamin Carbin, Michael Mansinghka, Vikash K. MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA

Modern probabilistic programming languages aim to formalize and automate key aspects of probabilistic modeling arid inference. Many languages provide constructs for programmable inference that enable developers to improve inference speed and accuracy by tailoring an algorithm for use with a particular model or dataset. Unfortunately, it is easy to use these constructs to write unsound programs that appear to run correctly but produce incorrect results. To address this problem, we present a denotational semantics for programmable inference in higher-order probabilistic programming languages, along with a type system that ensures that well-typed inference programs are sound by construction. A central insight is that the type of a probabilistic expression can track the space of its possible execution traces, not just the type of value that it returns. as these traces are often the objects that inference algorithms manipulate. We use our semantics and type system to establish soundness properties of custom inference programs that use constructs for variational, sequential Monte Carlo, importance sampling, and Markov chain Monte Carlo inference.

关键词： probabilistic programming type systems programmable inference

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Semantics of Higher-Order probabilistic Programs with Conditioning

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第POPL期4卷 1–29页

作者： Dahlqvist, Fredrik Kozen, Dexter UCL Comp Sci London England Imperial Coll London Elect & Elect Engn London England Cornell Univ Comp Sci Ithaca NY USA

We present a denotational semantics for higher-order probabilistic programs in terms of linear operators between Banach spaces. Our semantics is rooted in the classical theory of Banach spaces and their tensor products, but bears similarities with the well-known semantics of higher-order programs a la Scott through the use of ordered Banach spaces which allow definitions in terms of fixed points. Our semantics is a model of intuitionistic linear logic: it is based on a symmetric monoidal closed category of ordered Banach spaces which treats randomness as a linear resource, but by constructing an exponential comonad we can also accommodate non-linear reasoning. We apply our semantics to the verification of the classical Gibbs sampling algorithm.

关键词： probabilistic programming semantics type system

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Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

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G3-GENES GENOMES GENETICS 2020年第2期10卷 769-781页

作者： dos Santos, Jhonathan P. R. Fernandes, Samuel B. McCoy, Scott Lozano, Roberto Brown, Patrick J. Leakey, Andrew D. B. Buckler, Edward S. Garcia, Antonio A. F. Gore, Michael A. Cornell Univ Sch Integrat Plant Sci Plant Breeding & Genet Sect 358 Plant Sci Bldg Ithaca NY 14853 USA Cornell Univ Inst Genom Divers Ithaca NY 14853 USA Univ Sao Paulo Luiz de Queiroz Coll Agr Dept Genet Piracicaba SP Brazil Univ Illinois Dept Crop Sci Urbana IL 61801 USA Univ Illinois Inst Genom Biol Urbana IL 61801 USA Univ Illinois Dept Plant Biol Urbana IL 61801 USA Univ Calif Davis Dept Plant Sci Sect Agr Plant Biol Davis CA 95616 USA ARS USDA RW Holley Ctr Ithaca NY 14853 USA

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

关键词： Bayesian networks biomass sorghum genomic prediction indirect selection probabilistic programming Genomic Prediction GenPred Shared Data Resources

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Toward an idiomatic framework for cognitive robotics

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PATTERNS 2022年第7期3卷 100533页

作者： Damgaard, Malte Rormose Pedersen, Rasmus Bak, Thomas Aalborg Univ Dept Elect Syst Automat & Control DK-9220 Aalborg Denmark

Inspired by the "cognitive hourglass'' model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by encouraging cooperation and re-use of existing results. This is done by proposing a framework dividing development of cognitive architectures into a series of layers that can be considered partly in isolation, some of which directly relate to other research fields. Finally, we introduce and review some topics essential for the proposed framework. We also outline a set of applications.

关键词： robotics cognitive robotics cognitive architecture framework probabilistic programming probabilistic programming language probabilistic inference variational inference message-passing algorithm stochastic variational inference probabilistic graphical model

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On the Probability and Cost of Ignorance, Inconsistency, Nonsense and More

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2020年第5-6期34卷 423-450页

作者： Szalas, Andrzej Univ Warsaw Dept Math Informat & Mech Warsaw Poland Univ Linkoping Dept Comp & Informat Sci Linkoping Sweden

Ignorance, inconsistency, nonsense and similar phenomena are omnipresent in everyday reasoning. They have been intensively studied, especially in the area of multiple-valued logics. Therefore we develop a framework for belief bases, combining multiple-valued and probabilistic reasoning, with the main focus on the way belief bases are actually used and accessed through queries. As an implementation tool we use a probabilistic programming language PROBLOG. Though based on distribution semantics with the independence assumption, we show how its constructs can successfully be used in implementing the considered logics and belief bases. In particular, we develop a technique for shifting probabilistic dependencies to the level of symbolic parts of belief bases. We also discuss applications of the framework in reasoning with Likert-type scales, widely exploited in questionnaire-based experimental research in psychology, economics, sociology, politics, public opinion measurements, and related areas.

关键词： Multiple-valued logics doxastic reasoning probabilistic reasoning probabilistic programming belief bases reasoning with uncertainty

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Towards Verified Stochastic Variational Inference for probabilistic Programs

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第POPL期4卷 1–33页

作者： Lee, Wonyeol Yu, Hangyeol Rival, Xavier Yang, Hongseok Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea INRIA Paris Dept Informat ENS Paris France PSL Univ CNRS Paris France

probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engines. Recently its combination with deep learning has been explored intensely, which led to the development of so called deep probabilistic programming languages, such as Pyro, Edward and ProbTorch. At the core of this development lie inference engines based on stochastic variational inference algorithms. When asked to find information about the posterior distribution of a model written in such a language, these algorithms convert this posterior-inference query into an optimisation problem and solve it approximately by a form of gradient ascent or descent. In this paper, we analyse one of the most fundamental and versatile variational inference algorithms, called score estimator or REINFORCE, using tools from denotational semantics and program analysis. We formally express what this algorithm does on models denoted by programs, and expose implicit assumptions made by the algorithm on the models. The violation of these assumptions may lead to an undefined optimisation objective or the loss of convergence guarantee of the optimisation process. We then describe rules for proving these assumptions, which can be automated by static program analyses. Some of our rules use nontrivial facts from continuous mathematics, and let us replace requirements about integrals in the assumptions, such as integrability of functions defined in terms of programs' denotations, by conditions involving differentiation or boundedness, which are much easier to prove automatically (and manually). Following our general methodology, we have developed a static program analysis for the Pyro programming language that aims at discharging the assumption about what we call model-guide support match. Our analysis is applied to the eight representative model-guide pairs from the Pyro webpage, which include sophisticated neural ne

关键词： probabilistic programming static analysis semantics correctness

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Scaling Exact Inference for Discrete probabilistic Programs

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第OOPSLA期4卷 1–31页

作者： Holtzen, Steven Van den Broeck, Guy Millstein, Todd Univ Calif Los Angeles Los Angeles CA 90095 USA

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 is fundamentally hard, so there is no one-size-fits all solution. In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions are discrete. Discrete distributions are common in many fields, including text analysis, network verification, artificial intelligence, and graph analysis, but they prove to be challenging for existing PPLs. We develop a domain-specific probabilistic programming language called Dice that features a new approach to exact discrete probabilistic program inference. Dice exploits program structure in order to factorize inference, enabling us to perform exact inference on probabilistic programs with hundreds of thousands of random variables. Our key technical contribution is a new reduction from discrete probabilistic programs to weighted model counting (WMC). This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference. We (1) show how to compositionally reduce Dice inference to WMC, (2) prove this compilation correct with respect to a denotational semantics, (3) empirically demonstrate the performance benefits over prior approaches, and (4) analyze the types of structure that allow Dice to scale to large probabilistic programs.

关键词： probabilistic programming

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Designing Perceptual Puzzles by Differentiating probabilistic Programs 22

Designing Perceptual Puzzles by Differentiating Probabilisti...

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ACM SIGGRAPH 2022 Conference Proceedings

作者： Kartik Chandra Tzu-Mao Li Joshua Tenenbaum Jonathan Ragan-Kelley Computer Science & Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) United States of America Department of Computer Science and Engineering (CSE) University of California San Diego United States of America Department of Brain and Cognitive Sciences (BCS) Center for Brains Minds & Machines (CBMM) Computer Science & Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) United States of America

ISBN: (纸本)9781450393379

We design new visual illusions by finding “adversarial examples” for principled models of human perception — specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.

关键词： differentiable programming illusions probabilistic programming

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Understanding Human Generated Decision Data 1

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10th Annual International symposium on Business Modeling and Software Design (BMSD)

作者： Silvander, Johan Blekinge Inst Technol Software Engn Res Lab Sweden Karlskrona Sweden

ISBN: (数字)9783030523060

ISBN: (纸本)9783030523060;9783030523053

In order to design intent-driven systems, the understanding of how the data is generated is essential. Without the understanding of the data generation process, it is not possible to use interventions, and counterfactuals. Interventions, and counterfactuals, are useful tools in order to achieve an artificial intelligence which can improve the system itself. We will create an understanding, and a model, of how data about decisions are generated, as well as used, by human decision makers. The research data were collected with the help of focus group interviews, and questionnaires. The models were built and evaluated with the help of, bayesian statistics, probability programming, and discussions with the practitioners. When we are combining, probabilistic programming models, extended machine learning algorithms, and data science processes, into a directed acyclic graph, we can mimic the process of human generated decision data. We believe the usage of a directed acyclic graph, to combine the functions and models, is a good base for mimic human generated decision data. Our next step is to evaluate if flow-based programming can be used as a framework for realization of components, useful in intent-driven systems.

关键词： Human decisions Bayesian statistics probabilistic programming

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Bayesian protein superposition using Hamiltonian Monte Carlo 20

Bayesian protein superposition using Hamiltonian Monte Carlo

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20th IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Moreta, Lys Sanz Al-Sibahi, Ahmad Salim Hamelryck, Thomas Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Sect Computat & RNA Biol Bioinformat Ctr Copenhagen Denmark

ISBN: (纸本)9781728195742

Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1]-[3] which relies on maximum likelihood estimation. The probabilistic model interprets each protein as a rotated and translated noisy observation of a latent mean structure. Unlike conventional methods [4], THESEUS takes into account the differences in correlations between the atoms in the structure. This paper extends the previous THESEUS MAP (Maximum A Posteriori) model, [5] to full Bayesian inference by making use of the iterative NUTS [6], a Hamiltonian Monte Carlo method. The model delivers consistent results and is computationally efficient thanks to its implementation in the probabilistic programming language NumpPyro [7], [8] which in turn relies upon JAX [9], a system for high-performance machine learning.

关键词： protein superposition Bayesian modelling probabilistic programming NUTS Hamiltonian Monte Carlo protein structure superposition

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