I am a postdoctoral research scientist at the Columbia University Data Science Institute, working with David Blei. Title: Hierarchical Implicit Models and Likelihood-Free Variational Inference. Material adapted from David Blei jUMD Variational Inference 9 / 15. Professor of Statistics and Computer Science, Columbia University. David M. Blei DAVID.BLEI@COLUMBIA.EDU Columbia University, 500 W 120th St., New York, NY 10027 Abstract Black box variational inference allows re- searchers to easily prototype and evaluate an ar-ray of models. Shay Cohen, David Blei, Noah Smith Variational Inference for Adaptor Grammars 28/32. Cited by. David M. Blei's 252 research works with 67,259 citations and 7,152 reads, including: Double Empirical Bayes Testing David M. Blei blei@cs.princeton.edu Princeton University, 35 Olden St., Princeton, NJ 08540 Eric P. Xing epxing@cs.cmu.edu Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213 Abstract Stochastic variational inference nds good posterior approximations of probabilistic mod-els with very large data sets. As with most traditional stochas-tic optimization methods, … Title. Articles Cited by Co-authors. Sort by citations Sort by year Sort by title. DM Blei, AY Ng, … Copula variational inference Dustin Tran HarvardUniversity David M. Blei ColumbiaUniversity Edoardo M. Airoldi HarvardUniversity Abstract We develop a general variational inference … History 21/49 I Idea adapted fromstatistical physics{ mean- eld methods to t a neural network (Peterson and Anderson, 1987). David M. Blei Columbia University Abstract Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. Machine Learning Statistics Probabilistic topic models Bayesian nonparametrics Approximate posterior inference. Material adapted from David Blei j UMD Variational Inference j 6 / 29. Abstract . We assume additional parameters ↵ that are fixed. 2003). Christian A. Naesseth Scott W. Linderman Rajesh Ranganath David M. Blei Linköping University Columbia University New York University Columbia University Abstract Many recent advances in large scale probabilistic inference rely on variational methods. Sort. Stochastic Variational Inference . Variational inference for Dirichlet process mixtures David M. Blei School of Computer Science Carnegie Mellon University Michael I. Jordan Department of Statistics and Computer Science Division University of California, Berkeley Abstract. Jensen’s Inequality: Concave Functions and Expectations log(t á x 1 +(1! In this paper, we present a variational inference algorithm for DP mixtures. Material adapted from David Blei jUMD Variational Inference 8 / 15. Operator Variational Inference Rajesh Ranganath PrincetonUniversity Jaan Altosaar PrincetonUniversity Dustin Tran ColumbiaUniversity David M. Blei ColumbiaUniversity Year; Latent dirichlet allocation. Black Box variational inference, Rajesh Ranganath, Sean Gerrish, David M. Blei, AISTATS 2014 Keyonvafa’s blog Machine learning, a probabilistic perspective, by Kevin Murphy Authors: Dustin Tran, Rajesh Ranganath, David M. Blei. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations Wu Liny, Mohammad Emtiyaz Khan*, Mark Schmidty yUniversity of British Columbia, *RIKEN Center for AI Project wlin2018@cs.ubc.ca, emtiyaz.khan@riken.jp, schmidtm@cs.ubc.ca Abstract NIPS 2014 Workshop. Verified email at columbia.edu - Homepage. 13 December 2014 ♦ Level 5 ♦ Room 510 a Convention and Exhibition Center, Montreal, Canada. It uses stochastic optimization to fit a variational distribution, fol-lowing easy-to-compute noisy natural gradients. We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. Cited by. I Picked up by Jordan’s lab in the early 1990s, generalized it to many probabilistic models. Abstract Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian Advances in Variational Inference. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) David M. Blei Department of Statistics Department of Computer Science Colombia University david.blei@colombia.edu Abstract Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation to massive data. Add summary notes for … Online Variational Inference for the Hierarchical Dirichlet Process Chong Wang John Paisley David M. Blei Computer Science Department, Princeton University fchongw,jpaisley,bleig@cs.princeton.edu Abstract The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be used to model mixed-membership data with a poten- tially infinite number of components.