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Gen: a general-purpose probabilistic programming system with programmable inference
2019
Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2019
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This paper introduces Gen, a new probabilistic programming system with novel language constructs for modeling and for end-user customization and optimization of inference. ...
repeated computation, enabling speedups from caching; and (iv) a standard inference library that supports custom proposal distributions also written as programs in Gen. ...
Gen also makes it practical to write inference programs that combine built-in operators for Monte Carlo inference and gradient-based optimization with custom algorithmic proposals and deep inference networks ...
doi:10.1145/3314221.3314642
dblp:conf/pldi/Cusumano-Towner19
fatcat:bmjwmh7jhjf33gg6dywrdjop7y
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
[article]
2020
arXiv
pre-print
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PClean is powered by three modeling and inference contributions: (1) a non-parametric model of relational database instances, customizable via probabilistic programs, (2) a sequential Monte Carlo inference ...
Data cleaning can be naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which ...
Zia Abedjan, Marco Cusumano-Towner, Raul Castro Fernandez, Cameron Freer, Divya Gopinath, Christina Ji, Tim Kraska, George Matheos, Feras Saad, Michael Stonebraker, Josh Tenenbaum, and Veronica Weiner for ...
arXiv:2007.11838v4
fatcat:navjwv7vpfbzhaq4mugkbucvve
Picture: A probabilistic programming language for scene perception
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose ...
Picture provides a stochastic scene language that can express generative models for arbitrary 2D/3D scenes, as well as a hierarchy of representation layers for comparing scene hypotheses with observed ...
Acknowledgements We thank Thomas Vetter for giving us access to the Basel face model. T. Kulkarni was graciously supported by the Leventhal Fellowship. ...
doi:10.1109/cvpr.2015.7299068
dblp:conf/cvpr/KulkarniKTM15
fatcat:kwyidcyug5esxiu5jykt6knim4
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1810.00873v3 fatcat:e66ojrp6jbbllpz76cbztpgguqOther Versions
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. ...
Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. ...
INTRODUCTION Probabilistic Programming Languages (PPLs) are designed to describe probabilistic models and run inference on these models. ...
arXiv:1810.00873v5
fatcat:3lcvh6vr6rbxhaszuuuvjpsjuu
Program Analysis of Probabilistic Programs
[article]
2022
arXiv
pre-print
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No single inference algorithm can be used as a probabilistic programming back-end that is simultaneously reliable, efficient, black-box, and general. ...
Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. ...
The program as a whole can then be seen as inferring some variables (those defined in the parameters block) via HMC, and others (those drawn with pseudo-random number generators) via ancestral sampling ...
arXiv:2204.06868v1
fatcat:2dbonwruuzaopil4aijdeuz4mi
An Introduction to Probabilistic Programming
[article]
2021
arXiv
pre-print
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It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. ...
Inference requires methods that generate samples by repeatedly evaluating the program. ...
Most critically, while all of the authors were at Oxford together, three of them were explicitly supported at various times by the DARPA under its Probabilistic Programming for Advanced Machine Learning ...
arXiv:1809.10756v2
fatcat:cdcsscxbu5af7fpm5w6mjjiyra
Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming
2021
Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
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Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. ...
Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. ...
Acknowledgements The authors are greatful to the following people for their helpful feedback and encouragements: E. Bingham, K. Kate, Y. Mroueh, F. Obermeyer, A. Pauthier, and D. Phan. ...
doi:10.1145/3453483.3454058
fatcat:vhgiqkekjrc2xpponmwkojns4q
Website Morphing
2009
Marketing science (Providence, R.I.)
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"Morphing" involves automatically matching the basic "look and feel" of a website, not just the content, to cognitive styles. We infer cognitive styles from clickstream data with Bayesian updating. ...
V irtual advisors often increase sales for those customers who find such online advice to be convenient and helpful. ...
Recent developments in the bandit literature now make it feasible to include switching costs via fast generalized index heuristics (e.g., Dusonchet and Hongler 2006, Jun 2004) . ...
doi:10.1287/mksc.1080.0459
fatcat:yeejiwmwcjb3nk4jc5lydt7j5u
World model learning and inference
2021
Neural Networks
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Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. ...
We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. ...
The authors express special thanks to Kenji Doya (symposium organiser) and Mitsuo Kawato (designated discussant). H.G thanks to J De Havas for editing. ...
doi:10.1016/j.neunet.2021.09.011
pmid:34634605
fatcat:ovruawnvivgybmd6keltwmdisy
Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks
[article]
2017
arXiv
pre-print
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Our approach divides a program into basic (branchless) blocks and estimates the maximal and minimal energy consumption for each block using an evolutionary algorithm. ...
In this work we address this challenge from the software point of view and propose a novel parametric approach to estimating tight bounds on the energy consumed by program executions that are practical ...
We also thank Henk Muller, Principal Technologist, XMOS, for his help with the measurement boards, evaluation platform, benchmarks, and overall support. ...
arXiv:1601.02800v2
fatcat:islryqjzineetk2vdjiq4rms2u
Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks
[chapter]
2018
Lecture Notes in Computer Science
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In this work we address this challenge from the software point of view and propose a novel approach to estimating accurate parametric bounds on the energy consumed by program executions that are practical ...
Then it combines the obtained values according to the program control flow, using a safe static analysis, to infer functions that give both upper and lower bounds on the energy consumption of the whole ...
We also thank Henk Muller, Principal Technologist, XMOS, for his help with the measurement boards, evaluation platform, benchmarks, and overall support. ...
doi:10.1007/978-3-319-94460-9_4
fatcat:xk772uh3r5dppfig3lp56f3qim
Taming Reflection
2021
ACM Transactions on Software Engineering and Methodology
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We propose a new instrumentation-based approach to address this issue in a non-invasive way. ...
After that, it automatically instruments the app to replace reflective calls with their corresponding Java calls in a traditional paradigm. ...
The gen keyword specifies that the method generates a new object of type Method (i.e., it is a factory function). ...
doi:10.1145/3440033
fatcat:ijkwhqxanfdt5fwbuux44o4sjy
Network Environment Design for Autonomous Cyberdefense
[article]
2021
arXiv
pre-print
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This paper introduces a novel approach for network environment design and a software framework to address the fundamental problem that network defense cannot be defined as a single game with a simple set ...
Demonstrating that a specific RL algorithm can be effective for defending a network under certain conditions may not necessarily give insight about the performance of the algorithm when the threats, network ...
In addition, CybORG's models use finite state machines as opposed to FARLAND's richer probabilistic models via generative programs [9] . ...
arXiv:2103.07583v1
fatcat:entwvffalvgphhpgazjtum4djq
Memory-driven computing accelerates genomic data processing
[article]
2019
bioRxiv
pre-print
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Together with the MDC-inherent solutions for local data privacy, a new compute model can be projected pushing large scale NGS data processing and primary data analytics closer to the edge by directly combining ...
Even more impressive, pseudoalignment by near-optimal probabilistic RNA-seq quantification (kallisto) was accelerated by more than two orders of magnitude with identical accuracy and indicated 66% reduced ...
We thank Bill Hayes, Keith Packard, Patrick Demichel, Binoy Arnold, Robert Peter Haddad, Eric Wu, Chris Kirby for supporting us in running the experiments on the HPE infrastructure. ...
doi:10.1101/519579
fatcat:6xpx4xzg7zdcrp4tuodk5njcsq
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
[article]
2020
arXiv
pre-print
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The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. ...
A simple and efficient programming language could accelerate applications of machine learning in various fields. ...
For example, Cusumano and Mansinghka [23] proposed a design for a probabilistic programming language called Gen, which is embedded in Julia, which aims to be sufficiently expressive and performant for ...
arXiv:2003.10146v1
fatcat:f2ocidpu4rchnokkc46qzrjgyu
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