Rajesh Ranganath

According to our database1, Rajesh Ranganath authored at least 41 papers between 2009 and 2018.

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Bibliography

2018
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns.
NeuroImage, 2018

Opportunities in Machine Learning for Healthcare.
CoRR, 2018

Multiple Causal Inference with Latent Confounding.
CoRR, 2018

Noisin: Unbiased Regularization for Recurrent Neural Networks.
CoRR, 2018

Noisin: Unbiased Regularization for Recurrent Neural Networks.
Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Sequential Monte Carlo.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Proximity Variational Inference.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Automatic Differentiation Variational Inference.
Journal of Machine Learning Research, 2017

Deep and Hierarchical Implicit Models.
CoRR, 2017

Proximity Variational Inference.
CoRR, 2017

Hierarchical Implicit Models and Likelihood-Free Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Inference via \chi Upper Bound Minimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Deep Survival Analysis.
CoRR, 2016

Operator Variational Inference.
CoRR, 2016

Automatic Differentiation Variational Inference.
CoRR, 2016

The $χ$-Divergence for Approximate Inference.
CoRR, 2016

Operator Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Deep Survival Analysis.
Proceedings of the 1st Machine Learning in Health Care, 2016

Hierarchical Variational Models.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Variational Tempering.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.
JAMIA, 2015

Variational Gaussian Process.
CoRR, 2015

Hierarchical Variational Models.
CoRR, 2015

Dynamic Poisson Factorization.
CoRR, 2015

The Survival Filter: Joint Survival Analysis with a Latent Time Series.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Dynamic Poisson Factorization.
Proceedings of the 9th ACM Conference on Recommender Systems, 2015

The Population Posterior and Bayesian Modeling on Streams.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Automatic Variational Inference in Stan.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep Exponential Families.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Deep Exponential Families.
CoRR, 2014

Black Box Variational Inference.
CoRR, 2014

Deterministic Annealing for Stochastic Variational Inference.
CoRR, 2014

Hierarchical topographic factor analysis.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Black Box Variational Inference.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Bayesian Nonparametric Poisson Factorization for Recommendation Systems.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates.
Computer Speech & Language, 2013

An Adaptive Learning Rate for Stochastic Variational Inference.
Proceedings of the 30th International Conference on Machine Learning, 2013

2011
Unsupervised learning of hierarchical representations with convolutional deep belief networks.
Commun. ACM, 2011

2009
Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation.
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31, 2009

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates.
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009


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