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Program Highlights »
Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ Seaside Ballroom
Advances in Approximate Bayesian Inference
Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · James McInerney · Dustin Tran · Dustin Tran · David Blei · Max Welling · Tamara Broderick · Michalis Titsias

Workshop Home Page

Approximate inference is key to modern probabilistic modeling. Thanks to the availability of big data, significant computational power, and sophisticated models, machine learning has achieved many breakthroughs in multiple application domains. At the same time, approximate inference becomes critical since exact inference is intractable for most models of interest. Within the field of approximate Bayesian inference, variational and Monte Carlo methods are currently the mainstay techniques. For both methods, there has been considerable progress both on the efficiency and performance.

In this workshop, we encourage submissions advancing approximate inference methods. We are open to a broad scope of methods within the field of Bayesian inference. In addition, we also encourage applications of approximate inference in many domains, such as computational biology, recommender systems, differential privacy, and industry applications.

Introduction (Talk)
Invited talk: Iain Murray (TBA) (Talk)
Contributed talk: Learning Implicit Generative Models Using Differentiable Graph Tests (Talk)
Invited talk: Gradient Estimators for Implicit Models (Talk)
Industry talk: Variational Autoencoders for Recommendation (Talk)
Poster Spotlights (Spotlight)
Coffee Break and Poster Session 1 (Break)
Industry talk: Cedric Archambeau (TBA) (Talk)
Contributed talk: Variational Inference based on Robust Divergences (Talk)
Lunch Break (Break)
Poster Session
Contributed talk: Adversarial Sequential Monte Carlo (Talk)
Contributed talk: Scalable Logit Gaussian Process Classification (Talk)
Invited talk: Variational Inference in Deep Gaussian Processes (Talk)
Coffee Break and Poster Session 2 (Break)
Contributed talk: Taylor Residual Estimators via Automatic Differentiation (Talk)
Invited talk: Differential privacy and Bayesian learning (Talk)
Contributed talk: Frequentist Consistency of Variational Bayes (Talk)
Panel: On the Foundations and Future of Approximate Inference (Panel)