Rahul G. Krishnan
Rahul G. Krishnan
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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
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
Barrier Frank-Wolfe for marginal inference
RG Krishnan, S Lacoste-Julien, D Sontag
Advances in Neural Information Processing Systems, 532-540, 2015
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
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
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
RJ Chen, RG Krishnan
arXiv preprint arXiv:2203.00585, 2022
Neural pharmacodynamic state space modeling
ZM Hussain, RG Krishnan, D Sontag
International Conference on Machine Learning, 4500-4510, 2021
Max-margin learning with the Bayes factor.
RG Krishnan, A Khandelwal, R Ranganath, D Sontag
UAI, 896-905, 2018
Inference & Introspection in Deep Generative Models of Sparse Data
RG Krishnan, M Hoffman
Workshop for Advances in Approximate Bayesian Inference at NIPS 2016, 0
Hierarchical Optimal Transport for Comparing Histopathology Datasets
A Yeaton, RG Krishnan, R Mieloszyk, D Alvarez-Melis, G Huynh
arXiv preprint arXiv:2204.08324, 2022
Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation
RG Krishnan, S Cenci, L Bourouiba
Annals of Epidemiology 65, 1-14, 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
Learning predictive checklists from continuous medical data
Y Makhija, E De Brouwer, RG Krishnan
arXiv preprint arXiv:2211.07076, 2022
Partial Identification of Treatment Effects with Implicit Generative Models
V Balazadeh, V Syrgkanis, RG Krishnan
arXiv preprint arXiv:2210.08139, 2022
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
W Ren, R Zeng, T Wu, T Zhu, RG Krishnan
arXiv preprint arXiv:2208.02301, 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
Deep learning and the future of the Model for End‐Stage Liver Disease–sodium score
M Cooper, RG Krishnan, M Bhat
Liver Transplantation, 2022
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