News

• December 2017: We are pleased to have presented a tutorial on probabilistic programming to over 4000 participants at the NIPS 2017 conference.

• September 2017: Our paper AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms was accepted to NIPS 2017.

• August 2017: Invited talk at the Sequential Monte Carlo Workshop 2017 in Uppsala, Sweden.

• July 2017: We are pleased to be teaching at the Probabilistic Programming for Advanced Machine Learning summer school hosted by DARPA in Arlington, Virginia. The focus will be on AI-assisted data analysis with BayesDB — learning how to answer data analysis questions in minutes that normally take hours or days for experienced computational statisticians.

• June 2017: We presented tutorials on Probabilistic Programming and AI for Structured Business Data to an audience of over 200 participants at the O’Reilly Artificial Intelligence Conference, New York City.

• June 2017: Vikash presented at the Technology Strategy and Leadership (TSLRP) meeting at Intel, including Intel’s CEO, management committee, and fellows. We briefed the company on new opportunities in probabilistic AI, including our work on building analogs of TenserFlow and the GPU for probabilistic programing.

• June 2017: Invited talk on AI Assisted Data Analysis for Humanitarian Causes at Effective Altruism Global in Boston, MA.

• May 2017: Two new preprints, Time Series Structure Discovery via Probabilistic Program Synthesis and AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms, are now available on the arXiv.

• May 2017: We taught an AI-assisted data science workshop at the Boston Children’s hospital, using BayesDB to analyze data relevant for understanding the effect of childhood adversity on the developing brain; in collaboration with the Laboratories for Cognitive Neuroscience.

• May 2017: Invited talk at AI Advance, hosted by the Ethics and Governance of Artificial Intelligence Initiative at the Berkman Klein Center for Internet and Society, Harvard University.

• April 2017: We presented Detecting dependencies in sparse, multivariate databases at the AISTATS conference in Fort Lauderdale, Florida.

• March 2017: Check out our new pre-prints on Probabilistic search for structured data via probabilistic programming and nonparametric Bayes and Probabilistic programs for inferring the goals of autonomous agents.

• April 2017: Invited talk on AI-assisted data science at DALI 2017 in Tenerife, Canary Islands.

• January 2017: Participated in the DARPA Hackathon in Arlington, Virginia.

  We used automated model discovery in BayesDB and Gaussian process modeling in Venture for a collection of challenging data science tasks: imputing missing data, modeling the effect of intervention variables on outcome variables, modeling missingness processes, and statistical model criticism. Our solutions to these problems, developed over the course of two days, were competitive with the baseline solutions developed by statistics specialists over the span of several weeks.

• January 2017: Presented Encapsulating models and approximate inference programs in probabilistic modules at the POPL Workshop on Probabilistic Programming Semantics in Paris, France.

• December 2016: We have presented the following collection of conference and workshop papers at the NIPS conference in Barcelona, Spain:

  • A Probabilistic Programming Approach To Probabilistic Data Analysis, main conference;

  • Gaussian process structure learning via probabilistic inverse compilation, Workshop on Interpretable Machine Learning in Complex Systems;

  • Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming, Workshop on Approximate Inference;

  • Monitoring the errors of discriminative models with probabilistic programming, Workshop on Reliable Machine Learning in the Wild.

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