Below are a collection of talks, presentations, and podcasts presented by members of the Probabilistic Computing Project.
2021-01-17 On the Automatic Derivation of Importance Samplers from Pairs of Probabilistic Programs. Alex Lew at LAFI 2021.
2021-01-10: Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models. George Matheos at AABI.
2020-11-20 AI alignment, philosophical pluralism, and the relevance of non-Western philosophy Tan Zhi Xuan at Effective Altruism Global X EAGxAsia-Pacific 2020.
2020-08-26 The fast loaded dice roller: A near-optimal exact sampler for discrete probability distributions – Feras Saad at AISTATS 2020.
2020-01-23 Optimal approximate sampling from discrete probability distributions – Feras Saad at POPL 2020.
2020-01-23 Trace Types and Denotational Semantics for Sound Programmable Inference in Probabilistic Languages – Alex Lew at POPL 2020.
2019-09-14 Probabilistic scripts for automating common-sense tasks – Alex Lew at Strange Loop 2019.
2019-09-14 InferenceQL: AI for data engineers, without the math – Ulrich Schaechtle at Strange Loop 2019.
2019-09-13 New programming constructs for probabilistic AI – Marco Cusumano-Towner at Strange Loop 2019.
2019-07-25: Gen: A General-Purpose Probabilistic Programming System – Alex Lew at JuliaCon 2019.
2019-07-23: Cleaning Messy Data with Julia and Gen – Alex Lew at JuliaCon 2019.
2019-06-24: Gen: A General-Purpose Probabilistic Programming System with Programmable Inference – Marco Cusumano-Towner at PLDI 2019.
2019-03-11: Bias in, Bias Out: Building Better AI – SXSW panel discussion including Vikash Mansinghka (audio only)
2019-01-17: Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling – Feras Saad at POPL 2019.
2018-12-06: Gen: A Flexible System for Programming Probabilistic AI – Marco Cusumano-Towner at PROBPROG 2018.
2018-12-01: Probabilistic programming and meta-programming in Clojure – Vikash Mansinghka at ClojureConj 2018
2018-10-05: Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling – Feras Saad at PROBPROG 2018.
2018-10-05: Automated data modeling for science via Bayesian probabilistic program synthesis – Ulrich Schaechtle at PROBPROG 2018.
2018-06-22: Probabilistic Programming with Programmable Inference – Vikash Mansinghka at PLDI 2018.
2017-12-04: Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning – Joshua Tenenbaum and Vikash Mansinghka at NIPS 2017.
2017-06-04: AI Assisted Data Analysis for Humanitarian Causes – Vikash Mansinghka at Effective Altruism (EA) Global, Boston.
2016-12-01: Introducing model-based thinking into AI systems – Vikash Mansinghka and Ben Lorica on The O’Reilly Data Show Podcast.
2016-10-06: Probabilistic Programming for Augmented Intelligence – Vikash Mansinghka at the Simons Institute for Theory of Computing.
2016-10-04: Three Problems in Computable Probability Theory – Cameron Freer at the Simons Institute for Theory of Computing.
2016-03-15: Probabilistic Programming for Augmented Intelligence – Vikash Mansinghka at the MIT Media Lab.
2015-10-28: BayesDB: Query the Probable Implications of Data – Marco Cusumano-Towner and Feras Saad at the Future Programming Workshop.
2015-09-24: An Overview of Probabilistic Programming – Vikash Mansighka at Strange Loop.
2015-09-24: BayesDB: Query the Probable Implications of Data – Richard Tibbetts at Strange Loop.
2015-02-17: A Survey of Probabilistic Programming – Vikash Mansinghka at Columbia Data Science