D
David M. Blei
Researcher at Columbia University
Publications - 399
Citations - 122384
David M. Blei is an academic researcher from Columbia University. The author has contributed to research in topics: Inference & Topic model. The author has an hindex of 98, co-authored 378 publications receiving 111547 citations. Previous affiliations of David M. Blei include Columbia University Medical Center & Hewlett-Packard.
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Text-Based Ideal Points
TL;DR: The text-based ideal point model (TBIP) as mentioned in this paper is an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors.
Journal ArticleDOI
Discussion on the paper by Handcock, Raftery and Tantrum
Tom A. B. Snijders,Tony Robinson,Anthony C. Atkinson,Marco Riani,Isobel Claire Gormley,Thomas Brendan Murphy,Trevor Sweeting,David S. Leslie,Nicholas T. Longford,John T. Kent,Tony Lawrance,Edoardo M. Airoldi,Julian Besag,David M. Blei,Stephen E. Fienberg,Ronald L. Breiger,Carter T. Butts,Patrick Doreian,Vladimir Batagelj,Anuška Ferligoj,David Draper,Marijtje A. J. van Duijn,Katherine Faust,Miruna Petrescu-Prahova,Jonathan J. Forster,Andrew Gelman,Steven M. Goodreau,Priscilla E. Greenwood,Katharina Gruenberg,Brian Francis,Christian Hennig,Peter D. Hoff,David R. Hunter,Dirk Husmeier,Chris A. Glasbey,David Krackhardt,Jouni Kuha,Anders Skrondal,Andrew B. Lawson,Tim Futing Liao,Bruno Mendes,Gesine Reinert,Sylvia Richardson,Alex Lewin,D. M. Titterington,Stanley Wasserman,Adriano Velasque Werhli,Peter Ghazal +47 more
TL;DR: Leslie et al. as mentioned in this paper showed that the Markov chain Monte Carlo sampling scheme that was used results in extremely slow mixing, requiring 2 million iterations with only every 1000th iteration being used.
Proceedings Article
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
TL;DR: In this article, a turnkey solver for the risk minimization problem for relational data is proposed. But it is not shown to be effective in practice, and the sampling scheme used for model specification has a strong effect on model performance.