Rajesh Ranganath

Coming Soon

Articles

  • Operator Variational Inference Rajesh Ranganath, Jaan Altosaar, Dustin Tran, and David M. Blei. NIPS 2016. [pdf]

  • Deep Survival Analysis Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. MLHC 2016. [pdf]

  • Hierarchical Variational Models. Rajesh Ranganath, Dustin Tran, and David M. Blei. ICML 2016. [pdf]

  • Variational tempering. Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, and David M. Blei. AISTATS 2016. [pdf]

  • The Variational Gaussian Process. Dustin Tran, Rajesh Ranganath, and David M. Blei. ICLR 2016. [pdf]

  • The Population Posterior and Bayesian Modeling on Streams. James McInerney, Rajesh Ranganath, and David M. Blei. NIPS 2015. [pdf]

  • Automatic Variational Inference in Stan. Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, and David M. Blei. NIPS 2015. [pdf]

  • Dynamic Poisson Factorization. Laurent Charlin, Rajesh Ranganath, James McInerney, and David M. Blei. RecSys 2015. [pdf]

  • The Survival Filter: Joint Survival Analysis with a Latent Time Series. Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. UAI 2015. [pdf]

  • Risk Prediction for Chronic Kidney Disease Progression Using Heterogeneous Electronic Health Record Data and Time Series Analysis.. Adler Perotte, Rajesh Ranganath, Jamie Hirsch, David M. Blei, and Noemie Elhadad. Journal of the American Medical Informatics Association (JAMIA) 2015. [html]

  • Deep Exponential Families. Rajesh Ranganath, Linpeng Tang, Laurent Charlin, and David M.Blei. AISTATS 2015. [pdf] [supplement]

  • Hierarchical Topographic Factor Analysis. Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, and David M. Blei. IEEE Xplore, 4th International Workshop on Pattern Recognition in Neuroimaging. [pdf]

  • Bayesian nonparametric Poisson factorization for recommendation systems. Prem Gopalan, Francisco JR Ruiz, Rajesh Ranganath, and David M. Blei. AISTATS 2014. [pdf]

  • Black Box Variational Inference. Rajesh Ranganath, Sean Gerrish, and David M. Blei. AISTATS 2014. [pdf] [supplement]

  • Topographic Factor Analysis: a Bayesian model for inferring brain networks from neural data. Jeremy R. Manning, Rajesh Ranganath, Kenneth A. Norman, and David M. Blei. Plos One 9(5), 2014. [pdf]

  • An Adaptive Learning Rate for Stochastic Variational Inference. Rajesh Ranganath, Chong Wang, David M. Blei, and Eric P. Xing. ICML 2013. [pdf]

  • Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Rajesh Ranganath, Dan Jurafsky, and Daniel A. McFarland. Computer Speech and Language. vol. 27, no. 1, pp. 89-115, 2013, doi:10.1016/j.csl.2012.01.00. [fulltext]

  • Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. Communications of the ACM, vol. 54, no. 10, pp. 95-103, 2011. (Research Highlights) [pdf]

  • It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates. Rajesh Ranganath, Dan Jurafsky, and Dan McFarland. In Proceedings of EMNLP 2009. [pdf]

  • Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation. Dan Jurafsky, Rajesh Ranganath, and Dan McFarland. In Proceedings of NAACL HLT 2009. [pdf]

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009. (Best paper award: Best application paper) [pdf]