Timezone: »
Probabilistic models and algorithmic techniques for inference have become standard tools for interpreting data and building systems that learn from their experience. Growing out of an extensive body of work in machine learning, statistics, robotics, vision, artificial intelligence, neuroscience and cognitive science, rich probabilistic models and inference techniques have more recently spread to other branches of science and engineering, from astrophysics to climate science to marketing to web site personalization. This explosion is largely due to the development of probabilistic graphical models, which provide a formal lingua franca for modeling, and a common target for efficient inference algorithms.
However, even simple probabilistic models can require significant effort and specialized expertise to develop and use, frequently involving custom mathematics, algorithm design and software development. More innovative and useful models far outstrip the representational capacity of graphical models and their associated inference techniques. They are communicated using a mix of natural language, pseudo code, and formulas, often eliding crucial aspects such as fine-grained independence, abstraction and recursion, and are fit to data via special purpose, one-off inference algorithms.
PROBABILISTIC PROGRAMMING LANGUAGES aim to close this gap, going beyond graphical models in representational capacity while providing automatic probabilistic inference. Rather than marry statistics with graph theory, probabilistic programming marries Bayesian probability with universal computation. Instead of modeling joint distributions over a set of random variables, probabilistic programs model distributions over the execution histories of programs, including programs that analyze, transform and write other programs. Users specify a probabilistic model in its entirety (e.g., by writing code that generates a sample from the joint distribution) and inference follows automatically given the specification. These languages provide the full power of modern programming languages for describing complex distributions, and can enable reuse of libraries of models, support interactive modeling and formal verification, and provide a much-needed abstraction barrier to foster generic, efficient inference in universal model classes.
We believe that the probabilistic programming language approach within AI has the potential to fundamentally change the way we understand, design, build, test and deploy probabilistic systems. This approach has seen growing interest within AI over the last 10 years, and builds on over 40 years of work in range of diverse fields including mathematical logic, theoretical computer science, programming languages, as well as machine learning, computational statistics, systems biology, probabilistic AI. However, considerable engineering challenges need to be solved before these techniques can be broadly adopted --- such as making inference efficient and producing robust, general-purpose software for probabilistic programming --- as do foundational questions about the complexity of inference and learning.
Our 2008 NIPS workshop helped to create the probabilistic programming community. For our 2012 workshop, we propose to:
Assess and synthesize progress in probabilistic programming since 2008, including progress in languages like BLOG, Church, Figaro, ICL, Markov Logic, CSoft/Infer.NET and ProbLog
Organize the growing community around key technical problems and benchmarks, to spur and systematize progress in the development and analysis of probabilistic programming systems
Expose interested funding agencies to probabilistic programming research
Author Information
Vikash Mansinghka (Massachusetts Institute of Technology)
Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He served on DARPA’s Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation. He was an advisor to Google DeepMind and has co-founded two AI-related startups, one acquired and one currently operational.
Daniel Roy (U of Toronto; Vector)
Noah Goodman (Stanford University)
More from the Same Authors
-
2021 : DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning »
Alex Tamkin · Vincent Liu · Rongfei Lu · Daniel Fein · Colin Schultz · Noah Goodman -
2021 Spotlight: Towards a Unified Information-Theoretic Framework for Generalization »
Mahdi Haghifam · Gintare Karolina Dziugaite · Shay Moran · Dan Roy -
2021 : Learning to solve complex tasks by growing knowledge culturally across generations »
Michael Tessler · Jason Madeano · Pedro Tsividis · Noah Goodman · Josh Tenenbaum -
2022 : Lemma: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions »
Zhening Li · Gabriel Poesia Reis e Silva · Omar Costilla Reyes · Noah Goodman · Armando Solar-Lezama -
2022 : On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning »
Dilip Arumugam · Mark Ho · Noah Goodman · Benjamin Van Roy -
2022 : In the ZONE: Measuring difficulty and progression in curriculum generation »
Rose Wang · Jesse Mu · Dilip Arumugam · Natasha Jaques · Noah Goodman -
2023 Poster: Why think step by step? Reasoning emerges from the locality of experience »
Benjamin Prystawski · Michael Li · Noah Goodman -
2023 Poster: Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions »
Eric Zelikman · Qian Huang · Gabriel Poesia · Noah Goodman · Nick Haber -
2023 Poster: Interpretability at Scale: Identifying Causal Mechanisms in Alpaca »
Zhengxuan Wu · Atticus Geiger · Christopher Potts · Noah Goodman -
2023 Poster: Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning »
Alex Tamkin · Margalit Glasgow · Xiluo He · Noah Goodman -
2023 Poster: Learning to Compress Prompts with Gist Tokens »
Jesse Mu · Xiang Li · Noah Goodman -
2023 Poster: Understanding Social Reasoning in Language Models with Language Models »
Kanishk Gandhi · Jan-Philipp Franken · Tobias Gerstenberg · Noah Goodman -
2023 Oral: Why think step by step? Reasoning emerges from the locality of experience »
Benjamin Prystawski · Michael Li · Noah Goodman -
2022 : MATH-AI: Toward Human-Level Mathematical Reasoning »
Francois Charton · Noah Goodman · Behnam Neyshabur · Talia Ringer · Daniel Selsam -
2022 : Learning Mathematical Reasoning for Education »
Noah Goodman -
2022 : Invited Talk: Noah Goodman »
Noah Goodman -
2022 Poster: Assistive Teaching of Motor Control Tasks to Humans »
Megha Srivastava · Erdem Biyik · Suvir Mirchandani · Noah Goodman · Dorsa Sadigh -
2022 Poster: CLEVRER-Humans: Describing Physical and Causal Events the Human Way »
Jiayuan Mao · Xuelin Yang · Xikun Zhang · Noah Goodman · Jiajun Wu -
2022 Poster: Geoclidean: Few-Shot Generalization in Euclidean Geometry »
Joy Hsu · Jiajun Wu · Noah Goodman -
2022 Poster: Active Learning Helps Pretrained Models Learn the Intended Task »
Alex Tamkin · Dat Nguyen · Salil Deshpande · Jesse Mu · Noah Goodman -
2022 Poster: Foundation Posteriors for Approximate Probabilistic Inference »
Mike Wu · Noah Goodman -
2022 Poster: STaR: Bootstrapping Reasoning With Reasoning »
Eric Zelikman · Yuhuai Wu · Jesse Mu · Noah Goodman -
2022 Poster: DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision »
Alex Tamkin · Gaurab Banerjee · Mohamed Owda · Vincent Liu · Shashank Rammoorthy · Noah Goodman -
2022 Poster: Improving Intrinsic Exploration with Language Abstractions »
Jesse Mu · Victor Zhong · Roberta Raileanu · Minqi Jiang · Noah Goodman · Tim Rocktäschel · Edward Grefenstette -
2021 : Spotlight Talk: Learning to solve complex tasks by growing knowledge culturally across generations »
Noah Goodman · Josh Tenenbaum · Michael Tessler · Jason Madeano -
2021 : Multi-party referential communication in complex strategic games »
Jessica Mankewitz · Veronica Boyce · Brandon Waldon · Georgia Loukatou · Dhara Yu · Jesse Mu · Noah Goodman · Michael C Frank -
2021 : Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages »
Alexander Lew · Mathieu Huot · Vikash Mansinghka -
2021 Workshop: Meaning in Context: Pragmatic Communication in Humans and Machines »
Jennifer Hu · Noga Zaslavsky · Aida Nematzadeh · Michael Franke · Roger Levy · Noah Goodman -
2021 : Opening remarks »
Jennifer Hu · Noga Zaslavsky · Aida Nematzadeh · Michael Franke · Roger Levy · Noah Goodman -
2021 Poster: Emergent Communication of Generalizations »
Jesse Mu · Noah Goodman -
2021 Poster: The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization »
Mufan Li · Mihai Nica · Dan Roy -
2021 Poster: Contrastive Reinforcement Learning of Symbolic Reasoning Domains »
Gabriel Poesia · WenXin Dong · Noah Goodman -
2021 Poster: Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers »
Jeffrey Negrea · Blair Bilodeau · Nicolò Campolongo · Francesco Orabona · Dan Roy -
2021 Poster: Improving Compositionality of Neural Networks by Decoding Representations to Inputs »
Mike Wu · Noah Goodman · Stefano Ermon -
2021 Poster: Towards a Unified Information-Theoretic Framework for Generalization »
Mahdi Haghifam · Gintare Karolina Dziugaite · Shay Moran · Dan Roy -
2021 Poster: 3DP3: 3D Scene Perception via Probabilistic Programming »
Nishad Gothoskar · Marco Cusumano-Towner · Ben Zinberg · Matin Ghavamizadeh · Falk Pollok · Austin Garrett · Josh Tenenbaum · Dan Gutfreund · Vikash Mansinghka -
2021 Panel: The Consequences of Massive Scaling in Machine Learning »
Noah Goodman · Melanie Mitchell · Joelle Pineau · Oriol Vinyals · Jared Kaplan -
2020 Poster: Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel »
Stanislav Fort · Gintare Karolina Dziugaite · Mansheej Paul · Sepideh Kharaghani · Daniel Roy · Surya Ganguli -
2020 Poster: Online Bayesian Goal Inference for Boundedly Rational Planning Agents »
Tan Zhi-Xuan · Jordyn Mann · Tom Silver · Josh Tenenbaum · Vikash Mansinghka -
2020 Poster: Adaptive Gradient Quantization for Data-Parallel SGD »
Fartash Faghri · Iman Tabrizian · Ilia Markov · Dan Alistarh · Daniel Roy · Ali Ramezani-Kebrya -
2020 Poster: Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms »
Mahdi Haghifam · Jeffrey Negrea · Ashish Khisti · Daniel Roy · Gintare Karolina Dziugaite -
2020 Poster: In search of robust measures of generalization »
Gintare Karolina Dziugaite · Alexandre Drouin · Brady Neal · Nitarshan Rajkumar · Ethan Caballero · Linbo Wang · Ioannis Mitliagkas · Daniel Roy -
2020 Poster: Language Through a Prism: A Spectral Approach for Multiscale Language Representations »
Alex Tamkin · Dan Jurafsky · Noah Goodman -
2019 : Lunch break & Poster session »
Breandan Considine · Michael Innes · Du Phan · Dougal Maclaurin · Robin Manhaeve · Alexey Radul · Shashi Gowda · Ekansh Sharma · Eli Sennesh · Maxim Kochurov · Gordon Plotkin · Thomas Wiecki · Navjot Kukreja · Chung-chieh Shan · Matthew Johnson · Dan Belov · Neeraj Pradhan · Wannes Meert · Angelika Kimmig · Luc De Raedt · Brian Patton · Matthew Hoffman · Rif A. Saurous · Daniel Roy · Eli Bingham · Martin Jankowiak · Colin Carroll · Junpeng Lao · Liam Paull · Martin Abadi · Angel Rojas Jimenez · JP Chen -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 Workshop: Machine Learning with Guarantees »
Ben London · Gintare Karolina Dziugaite · Daniel Roy · Thorsten Joachims · Aleksander Madry · John Shawe-Taylor -
2019 : Posters »
Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner -
2019 Poster: Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates »
Jeffrey Negrea · Mahdi Haghifam · Gintare Karolina Dziugaite · Ashish Khisti · Daniel Roy -
2019 Poster: Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes »
Jun Yang · Shengyang Sun · Daniel Roy -
2019 Poster: Variational Bayesian Optimal Experimental Design »
Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman -
2019 Spotlight: Variational Bayesian Optimal Experimental Design »
Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman -
2018 Poster: Data-dependent PAC-Bayes priors via differential privacy »
Gintare Karolina Dziugaite · Daniel Roy -
2018 Poster: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Spotlight: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Poster: Multimodal Generative Models for Scalable Weakly-Supervised Learning »
Mike Wu · Noah Goodman -
2017 : Daniel Roy - Deep Neural Networks: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes »
Daniel Roy -
2017 : Morning panel discussion »
Jürgen Schmidhuber · Noah Goodman · Anca Dragan · Pushmeet Kohli · Dhruv Batra -
2017 : "Language in context" »
Noah Goodman -
2017 Poster: AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms »
Marco Cusumano-Towner · Vikash Mansinghka -
2017 Poster: Learning Disentangled Representations with Semi-Supervised Deep Generative Models »
Siddharth Narayanaswamy · Brooks Paige · Jan-Willem van de Meent · Alban Desmaison · Noah Goodman · Pushmeet Kohli · Frank Wood · Philip Torr -
2017 Tutorial: Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning »
Josh Tenenbaum · Vikash Mansinghka -
2016 Poster: A Probabilistic Programming Approach To Probabilistic Data Analysis »
Feras Saad · Vikash Mansinghka -
2016 Poster: Measuring the reliability of MCMC inference with bidirectional Monte Carlo »
Roger Grosse · Siddharth Ancha · Daniel Roy -
2016 Poster: Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks »
Daniel Ritchie · Anna Thomas · Pat Hanrahan · Noah Goodman -
2015 Workshop: Bounded Optimality and Rational Metareasoning »
Samuel J Gershman · Falk Lieder · Tom Griffiths · Noah Goodman -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Poster: Gibbs-type Indian Buffet Processes »
Creighton Heaukulani · Daniel Roy -
2014 Poster: Mondrian Forests: Efficient Online Random Forests »
Balaji Lakshminarayanan · Daniel Roy · Yee Whye Teh -
2013 Poster: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2013 Oral: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2013 Poster: Learning and using language via recursive pragmatic reasoning about other agents »
Nathaniel J Smith · Noah Goodman · Michael C Frank -
2013 Poster: Learning Stochastic Inverses »
Andreas Stuhlmüller · Jacob Taylor · Noah Goodman -
2013 Session: Session Chair »
Daniel Roy -
2013 Session: Tutorial Session B »
Daniel Roy -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Poster: Random function priors for exchangeable graphs and arrays »
James R Lloyd · Daniel Roy · Peter Orbanz · Zoubin Ghahramani -
2012 Poster: Burn-in, bias, and the rationality of anchoring »
Falk Lieder · Tom Griffiths · Noah Goodman -
2011 Poster: Complexity of Inference in Latent Dirichlet Allocation »
David Sontag · Daniel Roy -
2011 Spotlight: Complexity of Inference in Latent Dirichlet Allocation »
David Sontag · Daniel Roy -
2011 Poster: Nonstandard Interpretations of Probabilistic Programs for Efficient Inference »
David Wingate · Noah Goodman · Andreas Stuhlmueller · Jeffrey Siskind -
2009 Demonstration: Monte: An Interactive Ssytem for Massively Parallel Probabilistic Programming »
Vikash Mansinghka -
2009 Demonstration: The IID: A Natively Probabilistic Reconfigurable Computer »
Vikash Mansinghka -
2008 Workshop: Probabilistic Programming: Universal Languages, Systems and Applications »
Daniel Roy · John Winn · David A McAllester · Vikash Mansinghka · Josh Tenenbaum -
2008 Oral: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Poster: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2006 Poster: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Talk: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Demonstration: Blaise: A System for Interactive Development of High Performance Inference Algorithms »
Keith Bonawitz · Vikash Mansinghka