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
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*
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
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
Artificial Intelligence and Statistics (AISTATS), 2022

Clustering Interval-Censored Time-Series for Disease Phenotyping
I. Chen, R. Krishnan, D. Sontag
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
R. Krishnan, D. Liang, M. Hoffman
Artificial Intelligence and Statistics (AISTATS), 2018

Structured Inference Networks for Nonlinear State Space Models
R. Krishnan, U. Shalit, D. Sontag
Association for the Advancement of Artificial Intelligence (AAAI), 2017

Oral Presentation

Barrier Frank-Wolfe for Marginal Inference
R. Krishnan, S. Lacoste-Julien, D. Sontag
Neural Information Processing Systems (NeurIPS), 2015


Deep Kalman Filters
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
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