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Thompson Sampling Based Active Learning in probabilistic Programs with Application to Travel Time Estimation 32nd

Thompson Sampling Based Active Learning in Probabilistic Pro...

32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Glimsdal, Sondre Granmo, Ole-Christoffer Univ Agder Ctr Artificial Intelligence Res Grimstad Norway

ISBN: (纸本)9783030229993;9783030229986

The pertinent problem of Traveling Time Estimation (TTE) is to estimate the travel time, given a start location and a destination, solely based on the coordinates of the points under consideration. This is typically solved by fitting a function based on a sequence of observations. However, it can be expensive or slow to obtain labeled data or measurements to calibrate the estimation function. Active Learning tries to alleviate this problem by actively selecting samples that minimize the total number of samples needed to do accurate inference. probabilistic programming Languages (PPL) give us the opportunities to apply powerful Bayesian inference to model problems that involve uncertainties. In this paper we combine Thompson Sampling with probabilistic programming to perform Active Learning in the Travel Time Estimation setting, outperforming traditional active learning methods.

关键词： Thompson Sampling Distance Estimation probabilistic programming Active Learning Travel Time Estimation

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Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting 37

Synthesizing Tight Privacy and Accuracy Bounds via Weighted ...

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37th IEEE Computer Security Foundations Symposium (CSF)

作者： Oakley, Lisa Holtzen, Steven Oprea, Alina Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

ISBN: (纸本)9798350362046;9798350362039

Programmatically generating tight differential privacy (DP) bounds is a hard problem. Two core challenges are (1) finding expressive, compact, and efficient encodings of the distributions of DP algorithms, and (2) state space explosion stemming from the multiple quantifiers and relational properties of the DP definition. We address the first challenge by developing a method for tight privacy and accuracy bound synthesis using weighted model counting on binary decision diagrams, a state of the art technique from the artificial intelligence and automated reasoning communities for exactly computing probability distributions. We address the second challenge by developing a framework for leveraging inherent symmetries in DP algorithms. Our solution benefits from ongoing research in probabilistic programming languages, allowing us to succinctly and expressively represent different DP algorithms with approachable language syntax that can be used by non-experts. We provide a detailed case study of our solution on the binary randomized response algorithm. We also evaluate an implementation of our solution using the Dice probabilistic programming language for the randomized response and truncated geometric above threshold algorithms. We compare to prior work on exact DP verification using Markov chain probabilistic model checking and the decision procedure DiPC. Very few existing works consider mechanized analysis of accuracy guarantees for DP algorithms. We additionally provide a detailed analysis using our technique for finding tight accuracy bounds for DP algorithms.

关键词： Differential Privacy Weighted Model Counting probabilistic programming

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Guaranteed Inference for probabilistic Programs: A Parallelisable, Small-Step Operational Approach 25th

Guaranteed Inference for Probabilistic Programs: A Paralleli...

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25th International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI)

作者： Boreale, Michele Collodi, Luisa Univ Firenze Dipartimento Stat Informat Applicaz G Parenti Florence Italy

ISBN: (纸本)9783031505201;9783031505218

We put forward an approach to the semantics of probabilistic programs centered on an action-based language equipped with a small-step operational semantics. This approach provides benefits in terms of both clarity and effective implementation. Discrete and continuous distributions can be freely mixed, unbounded loops are allowed. In measure-theoretic terms, a product of Markov kernels is used to formalize the small-step operational semantics. This approach directly leads to an exact sampling algorithm that can be efficiently SIMD-parallelized. An observational semantics is also introduced based on a probability space of infinite sequences, along with a finite approximation theorem. Preliminary experiments with a proof-of-concept implementation based on TensorFlow show that our approach compares favourably to state-ofthe-art tools for probabilistic programming and inference.

关键词： probabilistic programming Operational semantics Measure theory Monte Carlo simulation SIMD parallelism

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probabilistic Multivariate Early Warning Signals 20th

Probabilistic Multivariate Early Warning Signals

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20th International Conference on Computational Methods in Systems Biology (CMSB)

作者： Laitinen, Ville Lahti, Leo Univ Turku Dept Comp Turku Finland

ISBN: (纸本)9783031150333;9783031150340

A broad range of natural and social systems from human microbiome to financial markets can go through critical transitions, where the system suddenly collapses to another stable configuration. Anticipating such transition early and accurately can facilitate controlled system manipulation and mitigation of undesired outcomes. Generic data-driven indicators, such as autocorrelation and variance, have been shown to increase in the vicinity of an approaching tipping point, and statistical early warning signals have been reported across a range of systems. In practice, obtaining reliable predictions has proven to challenging, as the available methods deal with simplified one-dimensional representations of complex systems, and rely on the availability of large amounts of data. Here, we demonstrate that a probabilistic data aggregation strategy can provide new ways to improve early warning detection by more efficiently utilizing the available information from multivariate time series. In particular, we consider a probabilistic variant of a vector autoregression model as a novel early warning indicator and argue that it has certain advantages in model regularization, treatment of uncertainties, and parameter interpretation. We evaluate the performance against alternatives in a simulation benchmark and show improved sensitivity in warning signal detection in a common ecological model encompassing multiple interacting species.

关键词： Early warning signals probabilistic programming Complex systems

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Language-Agnostic Static Analysis of probabilistic Programs 24

Language-Agnostic Static Analysis of Probabilistic Programs

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39th ACM/IEEE International Conference on Automated Software Engineering (ASE)

作者： Bock, Markus Schroder, Michael Cito, Jurgen TU Wien Vienna Austria

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

probabilistic programming allows developers to focus on the modeling aspect in the Bayesian workflow by abstracting away the posterior inference machinery. In practice, however, programming errors specific to the probabilistic environment are hard to fix without deep knowledge of the underlying systems. Like in classical software engineering, static program analysis methods could be employed to catch many of these errors. In this work, we present the first framework to formulate static analyses for probabilistic programs in a language-agnostic manner: LASAPP. While prior work focused on specific languages, all analyses written with our framework can be readily applied to new languages by adding easy-to-implement API bindings. Our prototype supports five popular probabilistic programming languages out-of-the-box. We demonstrate the effectiveness and expressiveness of the LASAPP framework by presenting four provably-correct language-agnostic probabilistic program analyses that address problems discussed in the literature and evaluate them on over 200 real-world programs.

关键词： probabilistic programming language-agnostic program analysis

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Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy 15

Higher-Order Approximate Relational Refinement Types for Mec...

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42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of programming Languages (POPL 15)

作者： Barthe, Gilles Gaboardi, Marco Arias, Emilio Jesus Gallego Hsu, Justin Roth, Aaron Strub, Pierre-Yves IMDEA Software Inst Madrid Spain Univ Dundee Dundee DD1 4HN Scotland Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781450333009

Mechanism design is the study of algorithm design where the inputs to the algorithm are controlled by strategic agents, who must be incentivized to faithfully report them. Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property-incentive properties are only useful if the strategic agents also believe this fact. Verification is an attractive way to convince agents that the incentive properties actually hold, but mechanism design poses several unique challenges: interesting properties can be sophisticated relational properties of probabilistic computations involving expected values, and mechanisms may rely on other probabilistic properties, like differential privacy, to achieve their goals. We introduce a relational refinement type system, called HOARe(2), for verifying mechanism design and differential privacy. We show that HOARe(2) is sound w.r.t. a denotational semantics, and correctly models (epsilon, delta)-differential privacy;moreover, we show that it subsumes DFuzz, an existing linear dependent type system for differential privacy. Finally, we develop an SMT-based implementation of HOARe(2) and use it to verify challenging examples of mechanism design, including auctions and aggregative games, and new proposed examples from differential privacy.

关键词： program logics probabilistic programming

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Estimating Outlier Score Probabilities 18

Estimating Outlier Score Probabilities

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IEEE 18th International Conference on Information Reuse and Integration (IRI)

作者： Bauder, Richard A. Khoshgoftaar, Taghi M. Florida Atlantic Univ Coll Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9781538615621

Outlier detection is a critical function across a diverse range of tasks and domains. There are numerous outlier detection methods, the majority of which produce scores to indicate an outlier versus inlier. An issue with these scores is that they can be difficult to interpret and do not allow for comparisons between different methods. One solution is to convert the outlier score to probabilities. These probability estimates can provide understandable and meaningful results for assessing outlying values. Moreover, the probabilities can be combined to produce an ensemble of outlier detection methods, further enhancing the detection of outliers. In this paper, we propose a unique approach leveraging probabilistic programming to fit the original outlier score distributions to a 3-parameter Lognormal distribution. We provide empirical evidence for the use of this distribution, compare the probability estimates with the outlier scores, discuss confidence in these estimates, evaluate detection performance via the probabilities, and provide an ensemble detection example. Our research indicates this approach reasonably models the original outlier scores, resulting in meaningful outlier probability estimates.

关键词： probabilistic programming outlier detection outlier probabilities outlier detection ensemble

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Scruff: A Deep probabilistic Cognitive Architecture for Predictive Processing 9th

Scruff: A Deep Probabilistic Cognitive Architecture for Pred...

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9th Annual International Conference of the Biologically-Inspired-Cognitive-Architectures-Society (BICA) on Biologically Inspired Cognitive Architectures (BICA) held as Part of the Joint Multi-Conference on Human-Level Artificial Intelligence (HLAI)

作者： Pfeffer, Avi Lynn, Spencer K. Charles River Analyt Inc Cambridge MA 02138 USA

ISBN: (纸本)9783319993164;9783319993157

The theory of predictive processing encompasses several elements that make it attractive as the underlying computational approach for a cognitive architecture. We introduce a new cognitive architecture, Scruff, capable of implementing predictive processing models by incorporating key properties of neural networks into the Bayesian probabilistic programming framework. We illustrate the Scruff approach with conditional linear Gaussian (CLG) models, noisy-or models, and a Bayesian variation of the Rao-Ballard linear algebra model of predictive vision.

关键词： probabilistic programming Predictive coding

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Bayesian inference with Stan: A tutorial on adding custom distributions

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BEHAVIOR RESEARCH METHODS 2017年第3期49卷 863-886页

作者： Annis, Jeffrey Miller, Brent J. Palmeri, Thomas J. Vanderbilt Univ 111 21st Ave S301 Wilson Hall Nashville TN 37240 USA

When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation;Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325-337, 2000) and JAGS (Plummer, 2003) provide "turnkey"-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153-178. doi:10.1016/***.2007.12.002,2008).

关键词： Bayesian inference Stan Linear ballistic accumulator probabilistic programming

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On a New Type of Neural Computation for probabilistic Symbolic Reasoning

On a New Type of Neural Computation for Probabilistic Symbol...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiao, Ximing Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781665488679

New types of neural computations, i.e., methods of computing neuron activities based on other neurons' activities and connectivity strengths, are continuously pushing the boundary of more powerful neural networks. For example, the attention mechanism with dynamic weight computation enables the Transformer-family models significantly surpass old models with fixed-weight transformations. We ask whether there exist more powerful types of neural computations that further surpass attention-based models, to reason under uncertainty and manipulate abstract symbols? Using probabilistic programming as the mathematical framework for capturing probabilistic and symbolic reasoning, we develop the first neural computation that allows the execution of probabilistic programs on neural networks. Evaluation on early language acquisition tasks shows the advantage of our method to learn abstract rules from raw data, a capability not possessed by classical neural networks.

关键词： neural networks neural computation reasoning probabilistic programming

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