618 citations · 1.9k across the 59 of their papers we have counts for
23 papers · 1 filter
FastSHAP: Real-Time Shapley Value Estimation
Neil Jethani, Mukund Sudarshan, Ian Covert +2
Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations. We introduce FastSHAP, a method for estima…
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs +1
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce th…
Probabilistic Machine Learning for Healthcare
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi +1
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review,…
Deep Direct Likelihood Knockoffs
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often…
Kernelized Complete Conditional Stein Discrepancy
Raghav Singhal, Xintian Han, Saad Lahlou +1
Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the un…
Support and Invertibility in Domain-Invariant Representations
Fredrik D. Johansson, David Sontag, Rajesh Ranganath
Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical resul…
Multiple Causal Inference with Latent Confounding
Rajesh Ranganath, Adler Perotte
Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assump…
Noisin: Unbiased Regularization for Recurrent Neural Networks
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar +1
Recurrent neural networks (RNNs) are powerful models of sequential data. They have been successfully used in domains such as text and speech. However, RNNs are susceptible to overf…
Proximity Variational Inference
Jaan Altosaar, Rajesh Ranganath, David M. Blei
Variational inference is a powerful approach for approximate posterior inference. However, it is sensitive to initialization and can be subject to poor local optima. In this paper,…
Variational Sequential Monte Carlo
Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath +1
Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric fami…
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran, Rajesh Ranganath, David M. Blei
Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for theories which encompass our understanding of the phy…
Variational Inference via -Upper Bound Minimization
Adji B. Dieng, Dustin Tran, Rajesh Ranganath +2
Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. It posits a family of approximating distributions and finds the closest membe…
Operator Variational Inference
Rajesh Ranganath, Jaan Altosaar, Dustin Tran +1
Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, variational inference uses the Kullback-Leibler divergence to d…
Deep Survival Analysis
Rajesh Ranganath, Adler Perotte, Noémie Elhadad +1
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we investigate surviva…
Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath +2
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to l…
The Variational Gaussian Process
Dustin Tran, Rajesh Ranganath, David M. Blei
Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variati…
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David M. Blei
Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However,…
The Population Posterior and Bayesian Inference on Streams
James McInerney, Rajesh Ranganath, David M. Blei
Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which a…
Correlated Random Measures
Rajesh Ranganath, David Blei
We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian n…
Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman +1
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes…
Deep Exponential Families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin +1
We describe \textit{deep exponential families} (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hie…
Variational Tempering
Stephan Mandt, James McInerney, Farhan Abrol +2
Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a det…
Black Box Variational Inference
Rajesh Ranganath, Sean Gerrish, David M. Blei
Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally re…