Search Results for author: Rahul G. Krishnan

Found 7 papers, 6 papers with code

Neural Pharmacodynamic State Space Modeling

2 code implementations22 Feb 2021 Zeshan Hussain, Rahul G. Krishnan, David Sontag

Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression.

Time Series

Clustering Left-Censored Multivariate Time-Series

no code implementations13 Feb 2021 Irene Y. Chen, Rahul G. Krishnan, David Sontag

To showcase the utility of our framework on real-world problems, we study how left-censorship can adversely affect the task of disease phenotyping, resulting in the often incorrect assumption that longitudinal patient data are aligned by disease stage.

Time Series

Variational Autoencoders for Collaborative Filtering

13 code implementations16 Feb 2018 Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.

Bayesian Inference Collaborative Filtering +2

On the challenges of learning with inference networks on sparse, high-dimensional data

1 code implementation17 Oct 2017 Rahul G. Krishnan, Dawen Liang, Matthew Hoffman

We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network.

Variational Inference

Structured Inference Networks for Nonlinear State Space Models

3 code implementations30 Sep 2016 Rahul G. Krishnan, Uri Shalit, David Sontag

We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.

Multivariate Time Series Forecasting

Deep Kalman Filters

3 code implementations16 Nov 2015 Rahul G. Krishnan, Uri Shalit, David Sontag

Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.

Counterfactual Inference Time Series

Barrier Frank-Wolfe for Marginal Inference

1 code implementation NeurIPS 2015 Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag

We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope.

Variational Inference

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