Research
Publications
Structured Neural Networks for Density Estimation and Causal Inference
A. Chen*, I. Shi*, X. Gao, R. Baptista*, R. Krishnan*
(Code)
Neural Information Processing Systems (NeurIPS), 2023
(*: equal contribution)
Copula-Based Deep Survival Models for Dependent Censoring
A. Foomani, M. Cooper, R. Grenier, R. Krishnan
(Code)
Uncertainty in Artificial Intelligence (UAI) 2023
DuETT: Dual Event Time Transformer for Electronic Health Records
A. Labach, A. Pokhrel, X. Huang, S. Zaberi, S. Yi, M. Volkovs, T. Poutanen, R. Krishnan
(Code)
Machine Learning for Healthcare (MLHC) 2023
Machine learning in computational histopathology: Challenges and Opportunities
M. Cooper, Z. Ji, R. Krishnan
Genes Cells and Chromosomes 2023
A Learning Based Hypothesis Test for Harmful Covariate Shift
T. Ginsberg, Z. Liang, R. Krishnan
(Code)
International Conference on Learning Representations (ICLR) 2023
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
E. de-Brouwer, R. Krishnan
(Code)
International Conference on Learning Representations (ICLR) 2023
Partial Identification with Implicit Generative Models
V. Belazadeh-Meresht, V. Syrgkanis, R. Krishnan
(Code)
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
Structured Neural Networks for Density Estimation
A.Chen*, I. Shi*, X. Gao, R. Baptista*, R. Krishnan*
Workshop on Structured Probabilistic Inference & Generative Modeling, ICML 2023
Learning predictive checklists from continuous medical data
Y. Makhija, E. de Brouwer, R. Krishnan
Machine Learning for Healthcare Workshop (ML4H), NeurIPS 2022
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