# Research

## Publications

Partial Identification with Implicit Generative Models

V. Belazadeh-Meresht, V. Syrgkanis, R. Krishnan

Neural Information Processing Systems (NeurIPS) 2022

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

W. Ren, R. Zeng, T. Wu, T. Zhu, R. Krishnan

(Code)

Machine Learning for Healthcare (MLHC) 2022

Large Images as Long Documents: Hierarchical ViTs with Self-Supervised Pretraining in Gigapixel Image Pyramids

R. Chen, C. Chen, Y. Li, T. Chen, A. Trister, R. Krishnan*, F. Mahmood*

(Code)

Computer Vision and Pattern Recognition (CVPR), 2022

(*: equal contribution)

Oral Presentation

Hierarchical Optimal Transport for Comparing Histopathology Datasets

A. Yeaton, R. Krishnan, R. Mieloszyk, D. Alvarez-Melis, G. Huynh

(Code)

Medical Imaging with Deep Learning (MIDL), 2022

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

R. Karlsson, M. Willbo, Z. Hussain, R. Krishnan, D. Sontag, F. Johansson

(Code)

Artificial Intelligence and Statistics (AISTATS), 2022

Clustering Interval-Censored Time-Series for Disease Phenotyping

I. Chen, R. Krishnan, D. Sontag

(Code)

Association for the Advancement of Artificial Intelligence (AAAI), 2022

Mitigating bias in estimating epidemic severity due toheterogeneity of epidemic on-set and data aggregation

R. Krishnan, S. Cenci, L. Bourouiba

In Press, Annals of Epidemiology, 2021

Neural Pharmacodynamic State Space Modeling

(Code) (Data)

Z. Hussain*, R. Krishnan*, D. Sontag

International Conference on Machine Learning (ICML), 2021

(*: equal contribution)

Max-Margin learning with the Bayes factor

R. Krishnan, A. Khandelwal, R. Ranganath, D. Sontag

Uncertainty in Artificial Intelligence (UAI), 2018

Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics

E. Lehman, R. Krishnan, X.Zhao, R. Mark, L. Lehman

Machine Learning for Healthcare (MLHC), 2018

Variational Autoencoders for Collaborative Filtering
(Code)

D. Liang, R. Krishnan, M. Hoffman, T. Jebara

World Wide Web Conference (WWW), 2018

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

(Code)

R. Krishnan, D. Liang, M. Hoffman

Artificial Intelligence and Statistics (AISTATS), 2018

Structured Inference Networks for Nonlinear State Space Models

(Code)

R. Krishnan, U. Shalit, D. Sontag

Association for the Advancement of Artificial Intelligence (AAAI), 2017

Oral Presentation

Barrier Frank-Wolfe for Marginal Inference

(Code)

R. Krishnan, S. Lacoste-Julien, D. Sontag

Neural Information Processing Systems (NeurIPS), 2015

## Preprints

Deep Kalman Filters

(Code)

R. Krishnan, U. Shalit, D. Sontag

Presented at Advances in Approximate Bayesian Inference & Black Box Inference (AABI) Workshop, NeurIPS, 2015

## Peer-reviewed workshop papers

Self-Supervised Vision Transformers Learn Disentangled Representations in Histopathology

R. Chen, R. Krishnan

(Code)

Learning Meaningful Representations of Life Workshop (LMRL), NeurIPS 2021

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

J. Wolff, T. Klein, M. Nabi, R. Krishnan, S. Nakajima

Bayesian Deep Learning Workshop (BDL), NeurIPS 2021

Inference and Introspection in Deep Generative Models of Sparse Non-Negative Data

R. Krishnan, M. Hoffman

Advances in Approximate Bayesian Inference & Black Box Inference (AABI) Workshop, NeurIPS, 2016

Disney Research Award

Early Detection of Diabetes from Health Claims

R. Krishnan, N. Razavian, Y. Choi, S. Nigam, S. Blecker, A. Schmidt, D. Sontag

Machine Learning in Healthcare Workshop, NeurIPS, 2013