Rahul G. Krishnan
Cited by
Cited by
Variational autoencoders for collaborative filtering
D Liang, RG Krishnan, MD Hoffman, T Jebara
Proceedings of the 2018 World Wide Web Conference, 689-698, 2018
Structured Inference Networks for Nonlinear State Space Models
RG Krishnan, U Shalit, D Sontag
arXiv preprint arXiv:1609.09869, 2016
Deep Kalman Filters
RG Krishnan, U Shalit, D Sontag
arXiv preprint arXiv:1511.05121, 2015
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
RJ Chen, C Chen, Y Li, TY Chen, AD Trister, RG Krishnan, F Mahmood
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
On the challenges of learning with inference networks on sparse, high-dimensional data
RG Krishnan, D Liang, MD Hoffman
The 21st International Conference on Artificial Intelligence and Statistics, 2018
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
RJ Chen, RG Krishnan
arXiv preprint arXiv:2203.00585, 2022
Barrier Frank-Wolfe for marginal inference
RG Krishnan, S Lacoste-Julien, D Sontag
Advances in Neural Information Processing Systems, 532-540, 2015
Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding
A Toma, PR Lawler, J Ba, RG Krishnan, BB Rubin, B Wang
arXiv preprint arXiv:2305.12031, 2023
Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics
EP Lehman, RG Krishnan, X Zhao, RG Mark, HL Li-wei
Machine Learning for Healthcare Conference, 571-586, 2018
Early detection of diabetes from health claims
R Krishnan, N Razavian, Y Choi, S Nigam, S Blecker, A Schmidt, ...
Machine Learning in Healthcare Workshop, NIPS, 1-5, 2013
Partial identification of treatment effects with implicit generative models
V Balazadeh Meresht, V Syrgkanis, RG Krishnan
Advances in Neural Information Processing Systems 35, 22816-22829, 2022
Neural pharmacodynamic state space modeling
ZM Hussain, RG Krishnan, D Sontag
International Conference on Machine Learning, 4500-4510, 2021
Machine learning in computational histopathology: Challenges and opportunities
M Cooper, Z Ji, RG Krishnan
Genes, Chromosomes and Cancer 62 (9), 540-556, 2023
A Learning Based Hypothesis Test for Harmful Covariate Shift
T Ginsberg, Z Liang, RG Krishnan
arXiv preprint arXiv:2212.02742, 2022
Clustering Interval-Censored Time-Series for Disease Phenotyping
IY Chen, RG Krishnan, D Sontag
Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6211-6221, 2022
Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?
A Hibi, M Jaberipour, MD Cusimano, A Bilbily, RG Krishnan, RI Aviv, ...
Medicine 101 (47), e31848, 2022
Hierarchical Optimal Transport for Comparing Histopathology Datasets
A Yeaton, RG Krishnan, R Mieloszyk, D Alvarez-Melis, G Huynh
arXiv preprint arXiv:2204.08324, 2022
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
W Ren, R Zeng, T Wu, T Zhu, RG Krishnan
Machine Learning for Healthcare Conference, 198-223, 2022
Learning predictive checklists from continuous medical data
Y Makhija, E De Brouwer, RG Krishnan
arXiv preprint arXiv:2211.07076, 2022
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
R Karlsson, M Willbo, Z Hussain, RG Krishnan, D Sontag, FD Johansson
arXiv preprint arXiv:2110.14993, 2021
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