Search Results for author: Rahul G. Krishnan

Found 16 papers, 12 papers with code

A Learning Based Hypothesis Test for Harmful Covariate Shift

1 code implementation6 Dec 2022 Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan

The ability to quickly and accurately identify covariate shift at test time is a critical and often overlooked component of safe machine learning systems deployed in high-risk domains.

Learning predictive checklists from continuous medical data

no code implementations14 Nov 2022 Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan

Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpretability.

Partial Identification of Treatment Effects with Implicit Generative Models

1 code implementation14 Oct 2022 Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan

We propose a new method for partial identification of average treatment effects(ATEs) in general causal graphs using implicit generative models comprising continuous and discrete random variables.

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

1 code implementation3 Aug 2022 Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan

One of the challenges in curriculum learning is the design of curricula -- i. e., in the sequential design of tasks that gradually increase in difficulty.

Medical Code Prediction

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

1 code implementation CVPR 2022 Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood

Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e. g. - 256x256, 384384).

Self-Supervised Learning Survival Prediction

Hierarchical Optimal Transport for Comparing Histopathology Datasets

no code implementations18 Apr 2022 Anna Yeaton, Rahul G. Krishnan, Rebecca Mieloszyk, David Alvarez-Melis, Grace Huynh

Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels.

Transfer Learning Type prediction

Mixture-of-experts VAEs can disregard variation in surjective multimodal data

no code implementations11 Apr 2022 Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima

Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthcare.

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology

1 code implementation1 Mar 2022 Richard J. Chen, Rahul G. Krishnan

Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology.

Knowledge Distillation Transfer Learning

Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

1 code implementation28 Oct 2021 Rickard K. A. Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David Sontag, Fredrik D. Johansson

Our question is when using this privileged data leads to more sample-efficient learning of models that use only baseline data for predictions at test time.

Time Series

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 Interval-Censored Time-Series for Disease Phenotyping

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

In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping.

Time Series

Variational Autoencoders for Collaborative Filtering

15 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

Cannot find the paper you are looking for? You can Submit a new open access paper.