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12th ACM Conference on Recommender Systems (RecSys)

作者： Tomkins, Sabina Isley, Steven London, Ben Getoor, Lise UC Santa Cruz Santa Cruz CA 95064 USA Amazon Seattle WA USA

ISBN: (纸本)9781450359016

Finding sustainable products and evaluating their claims is a significant barrier facing sustainability-minded customers. Tools that reduce both these burdens are likely to boost the sale of sustainable products. However, it is difficult to determine the sustainability characteristics of these products - there are a variety of certifications and definitions of sustainability, and quality labeling requires input from domain experts. In this paper, we propose a flexible probabilistic framework that uses domain knowledge to identify sustainable products and customers, and uses these labels to predict customer purchases. We evaluate our approach on grocery items from the Amazon catalog. Our proposed approach outperforms established recommender system models in predicting future purchases while jointly inferring sustainability scores for customers and products.

关键词： Sustainability probabilistic programming

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Relaxations for probabilistically constrained stochastic programming problems: review and extensions

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Annals of Operations Research 2018年 1-22页

作者： Lejeune, Miguel A. Prékopa, A. Department of Decision Sciences George Washington University Washington DC United States Rutgers University Piscataway NJ United States

We consider probabilistically constrained stochastic programming problems, in which the random variables are in the right-hand sides of the stochastic inequalities defining the joint chance constraints. Problems of that kind arise in a variety of contexts, and are particularly difficult to solve for random variables with continuous joint distributions, because the calculation of the cumulative distribution function and its gradient values involves numerical integration and/or simulation in large dimensional spaces. We revisit known and provide new relaxations extensions to various probability bounding schemes that permit to approximate the feasible set of joint probabilistic constraints. The derived mathematical formulations relax the requirement to handle large multivariate cumulative distribution functions and involve instead the computation of marginal and bivariate cumulative distribution functions. We analyze the convexity of and computational challenges posed by the inferred relaxations © 2018 Springer Science+Business Media, LLC, part of Springer Nature

关键词： Chance constraints probabilistic programming Relaxation and approximation problems Reliability Stochastic programming

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AcMC 2 : Accelerating Markov Chain Monte Carlo Algorithms for probabilistic Models 19

AcMC 2 : Accelerating Markov Chain Monte Carlo Algorithms fo...

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Proceedings of the Twenty-Fourth International Conference on Architectural Support for programming Languages and Operating Systems

作者： Subho S. Banerjee Zbigniew T. Kalbarczyk Ravishankar K. Iyer University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (纸本)9781450362405

probabilistic models (PMs) are ubiquitously used across a variety of machine learning applications. They have been shown to successfully integrate structural prior information about data and effectively quantify uncertainty to enable the development of more powerful, interpretable, and efficient learning algorithms. This paper presents AcMC2, a compiler that transforms PMs into optimized hardware accelerators (for use in FPGAs or ASICs) that utilize Markov chain Monte Carlo methods to infer and query a distribution of posterior samples from the model. The compiler analyzes statistical dependencies in the PM to drive several optimizations to maximally exploit the parallelism and data locality available in the problem. We demonstrate the use of AcMC2 to implement several learning and inference tasks on a Xilinx Virtex-7 FPGA. AcMC2-generated accelerators provide a 47-100× improvement in runtime performance over a 6-core IBM Power8 CPU and a 8-18× improvement over an NVIDIA K80 GPU. This corresponds to a 753-1600× improvement over the CPU and 248-463× over the GPU in performance-per-watt terms.

关键词： probabilistic programming accelerator probabilistic graphical models markov chain monte carlo

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probabilistic programming for assessment of capability and capacity

Probabilistic programming for assessment of capability and c...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XX

作者： Pfeffer, Avi P. Harrison, Scott A. Charles River Analyt Inc Cambridge MA 02138 USA

ISBN: (纸本)9780819486240

Answering the questions "What can the adversary do?" and "What will the adversary do?" are critical functions of intelligence analysis. These questions require processing many sources of information, which is currently performed manually by analysts, leading to missed opportunities and potential mistakes. We have developed a system for Assessment of Capability and Capacity via Intelligence Analysis (ACACIA) to help analysts assess the capability, capacity, and intention of a nation state or non-state actor. ACACIA constructs a Bayesian network (BN) to model the objectives and means of an actor in a situation. However, a straightforward BN implementation is insufficient, since objectives and means are different in every situation. Additionally, we wish to apply knowledge about an element gained from one situation to another situation containing the same element. Furthermore, different elements of the same kind usually share the same model structure with different parameters. We use the probabilistic programming language Figaro, which allows models to be constructed using the power of programming languages, to address these issues, generating BNs for diverse situations while maximizing sharing. We learn the parameters of a program from training instances. Experiments show ACACIA is capable of making accurate inferences and that learning effectively improves ACACIA's performance.

关键词： Intelligence analysis capability and capacity decision support system probabilistic reasoning Bayesian networks probabilistic programming machine learning

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Bayesian hierarchical spatial models: Implementing the Besag York Mollie model in stan

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SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY 2019年 31卷 100301-000页

作者： Morris, Mitzi Wheeler-Martin, Katherine Simpson, Dan Mooney, Stephen J. Gelman, Andrew DiMaggio, Charles Columbia Univ Inst Social & Econ Res & Policy New York NY USA NYU Dept Surg Sch Med New York NY 10016 USA Univ Toronto Dept Stat Sci Toronto ON Canada Univ Washington Dept Epidemiol Seattle WA 98195 USA Columbia Univ Dept Stat New York NY USA

This report presents a new implementation of the Besag-York-Mollie (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring schoolage pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Bayesian inference Intrinsic conditional auto-regressive model Besag-York-Mollie model probabilistic programming Stan Pedestrian injuries

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Automatic Differentiation Variational Inference

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JOURNAL OF MACHINE LEARNING RESEARCH 2017年第1期18卷 1-45页

作者： Kucukelbir, Alp Tran, Dustin Ranganath, Rajesh Gelman, Andrew Blei, David M. Columbia Univ Dept Comp Sci Data Sci Inst New York NY 10027 USA Columbia Univ Dept Comp Sci New York NY 10027 USA Princeton Univ Dept Comp Sci Princeton NJ 08540 USA Columbia Univ Data Sci Inst Dept Polit Sci New York NY 10027 USA Columbia Univ Data Sci Inst Dept Stat New York NY 10027 USA

probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, which makes it difficult to efficiently cycle through the steps. To this end, we develop automatic differentiation variational inference (ADVI). Using our method, the scientist only provides a probabilistic model and a dataset, nothing else. ADVI automatically derives an efficient variational inference algorithm, freeing the scientist to refine and explore many models. ADVI supports a broad class of models-no conjugacy assumptions are required. We study ADVI across ten modern probabilistic models and apply it to a dataset with millions of observations. We deploy ADVI as part of Stan, a probabilistic programming system.

关键词： Bayesian inference approximate inference probabilistic programming

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Planning in hybrid relational MDPs

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MACHINE LEARNING 2017年第12期106卷 1905-1932页

作者： Nitti, Davide Belle, Vaishak De Laet, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci B-3001 Leuven Belgium Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland Katholieke Univ Leuven Fac Engn Sci B-3001 Leuven Belgium

We study planning in relational Markov decision processes involving discrete and continuous states and actions, and an unknown number of objects. This combination of hybrid relational domains has so far not received a lot of attention. While both relational and hybrid approaches have been studied separately, planning in such domains is still challenging and often requires restrictive assumptions and approximations. We propose HYPE: a sample-based planner for hybrid relational domains that combines model-based approaches with state abstraction. HYPE samples episodes and uses the previous episodes as well as the model to approximate the Q-function. In addition, abstraction is performed for each sampled episode, this removes the complexity of symbolic approaches for hybrid relational domains. In our empirical evaluations, we show that HYPE is a general and widely applicable planner in domains ranging from strictly discrete to strictly continuous to hybrid ones, handles intricacies such as unknown objects and relational models. Moreover, empirical results showed that abstraction provides significant improvements.

关键词： MDP probabilistic planning Logic programming Relational MDP Hybrid Hybrid relational MDP probabilistic programming Abstraction Logical regression Importance sampling

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DERIVING PROBABILITY DENSITY FUNCTIONS FROM probabilistic FUNCTIONAL PROGRAMS

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LOGICAL METHODS IN COMPUTER SCIENCE 2017年第2期13卷

作者： Bhat, Sooraj Borgstrom, Johannes Gordon, Andrew D. Russo, Claudio Georgia Inst Technol Atlanta GA 30332 USA Uppsala Univ Uppsala Sweden Microsoft Res Redmond WA USA Univ Edinburgh Edinburgh Midlothian Scotland

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.

关键词： probability density function probabilistic programming Markov chain Monte Carlo

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FairSquare: probabilistic Verification of Program Fairness

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2017年第OOPSLA期1卷 1-30页

作者： Albarghouthi, Aws D'Antoni, Loris Drews, Samuel Nori, Aditya, V Univ Wisconsin Dept Comp Sci 1210 West Dayton St Madison WI 53706 USA Microsoft Res 21 Stn Rd Cambridge CB1 2FB England

With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate fairness and bias in decision-making programs. First, we show that a number of recently proposed formal definitions of fairness can be encoded as probabilistic program properties. Second, with the goal of enabling rigorous reasoning about fairness, we design a novel technique for verifying probabilistic properties that admits a wide class of decision-making programs. Third, we present FairSquare, the first verification tool for automatically certifying that a program meets a given fairness property. We evaluate FairSquare on a range of decision-making programs. Our evaluation demonstrates FairSquare's ability to verify fairness for a range of different programs, which we show are out-of-reach for state-of-the-art program analysis techniques.

关键词： Algorithmic Fairness probabilistic programming probabilistic Inference

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programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2017年第2期26卷 403-413页

作者： de Valpine, Perry Turek, Daniel Paciorek, Christopher J. Anderson-Bergman, Clifford Lang, Duncan Temple Bodik, Rastislav Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ Calif Davis Dept Stat Davis CA 95616 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This article provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM). Supplementary materials for this article are available online.

关键词： Domain-specific language Hierarchical models MCEM MCMC probabilistic programming R

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