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

Fri Dec 8th 08:00 AM -- 06:30 PM @ Seaside Ballroom
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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.

08:30 AM Introduction (Talk) Cheng Zhang, Francisco Ruiz, Dustin Tran, James McInerney, Stephan Mandt
08:35 AM Invited talk: Iain Murray (TBA) (Talk) Iain Murray
09:00 AM Contributed talk: Learning Implicit Generative Models Using Differentiable Graph Tests (Talk) Josip Djolonga
09:15 AM Invited talk: Gradient Estimators for Implicit Models (Talk) Yingzhen Li
09:40 AM Industry talk: Variational Autoencoders for Recommendation (Talk) Dawen Liang
10:00 AM Poster Spotlights (Spotlight)
Francesco Locatello, Ari Pakman, Da Tang, Tom Rainforth, Zalán Borsos, Marko Järvenpää, Eric Nalisnick, Gabriele Abbati, XIAOYU LU, Jonathan Huggins, Rachit Singh, Rui Luo
10:30 AM Coffee Break and Poster Session 1 (Break)
11:25 AM Industry talk: Cedric Archambeau (TBA) (Talk) Cedric Archambeau
11:45 AM Contributed talk: Variational Inference based on Robust Divergences (Talk) Futoshi Futami
12:00 PM Lunch Break (Break)
01:00 PM Poster Session <span> <a href="#"></a> </span>
Shunsuke Horii, Heejin Jeong, Tobias Schwedes, Qing He, Ben Calderhead, Ertunc Erdil, Jaan Altosaar, Patrick Muchmore, Rajiv Khanna, Ian Gemp, Pengfei Zhang, Yuan Zhou, Chris Cremer, Maria DeYoreo, Alexander Terenin, Brendan McVeigh, Rachit Singh, Yaodong Yang, Erik Bodin, Trefor Evans, Henry Chai, Shandian Zhe, Jeffrey Ling, Vincent ADAM, Lars Maaløe, Andrew Miller, Ari Pakman, Josip Djolonga, Hong Ge
02:05 PM Contributed talk: Adversarial Sequential Monte Carlo (Talk) Kira Kempinska
02:20 PM Contributed talk: Scalable Logit Gaussian Process Classification (Talk) Florian Wenzel
02:35 PM Invited talk: Variational Inference in Deep Gaussian Processes (Talk) Andreas Damianou
03:00 PM Coffee Break and Poster Session 2 (Break)
03:30 PM Contributed talk: Taylor Residual Estimators via Automatic Differentiation (Talk) Andrew Miller
03:45 PM Invited talk: Differential privacy and Bayesian learning (Talk) Antti Honkela
04:10 PM Contributed talk: Frequentist Consistency of Variational Bayes (Talk) Yixin Wang
04:25 PM Panel: On the Foundations and Future of Approximate Inference (Panel) David Blei, Zoubin Ghahramani, Katherine Heller, Tim Salimans, Max Welling, Matthew D. Hoffman

Author Information

Francisco Ruiz (Columbia University)
Stephan Mandt (Disney Research)
Cheng Zhang (Microsoft Research, Cambridge)
James McInerney (Spotify Research)
James McInerney (Spotify)
Dustin Tran (Columbia University & OpenAI)
Dustin Tran (Google Brain)
David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

Max Welling (University of Amsterdam / Qualcomm AI Research)
Tamara Broderick (MIT)
Michalis Titsias (DeepMind)

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