Rahul G. Krishnan

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David SontagProfessor, Massachusetts Institute of TechnologyVerified email at csail.mit.edu
Matthew D. HoffmanResearch Scientist, Google ResearchVerified email at google.com
Dawen LiangResearch Scientist, Netflix Inc.Verified email at netflix.com
Uri ShalitAssistant Professor, Technion - Israel Institute of TechnologyVerified email at technion.ac.il
Tony JebaraSpotifyVerified email at spotify.com
Richard J. ChenPhD Student, Harvard UniversityVerified email at g.harvard.edu
Andrew D. TristerHead of Digital Health, Gates FoundationVerified email at gatesfoundation.org
Faisal MahmoodAssociate Professor, Harvard Medical School / Brigham and Women's HospitalVerified email at fas.harvard.edu
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Xiaopeng ZhaoProfessor, University of TennesseeVerified email at utk.edu
Eric LehmanMITVerified email at mit.edu
Zeshan M HussainMD-PhD Student, Harvard Medical School, MITVerified email at mit.edu
Narges RazavianNew York University Medical CenterVerified email at nyumc.org
Youngduck ChoiCo-founder and CEO, TogiVerified email at togi.co
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
Fredrik D. JohanssonAssistant Professor, Chalmers University of TechnologyVerified email at chalmers.se
Rickard KarlssonDelft University of TechnologyVerified email at tudelft.nl
David Alvarez-MelisSenior Researcher, Microsoft ResearchVerified email at seas.harvard.edu
Vasilis SyrgkanisAssistant Professor, Stanford UniversityVerified email at stanford.edu
Irene Y. ChenAssistant Professor, UC Berkeley and UCSFVerified email at berkeley.edu

Rahul G. Krishnan

University of Toronto

Verified email at cs.toronto.edu - Homepage

Machine Learning Artificial Intelligence Healthcare


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Variational autoencoders for collaborative filtering D Liang, RG Krishnan, MD Hoffman, T Jebara Proceedings of the 2018 World Wide Web Conference, 689-698, 2018	887	2018
Structured Inference Networks for Nonlinear State Space Models RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1609.09869, 2016	424	2016
Deep Kalman Filters RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1511.05121, 2015	354	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	77	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	76	2018
Barrier Frank-Wolfe for marginal inference RG Krishnan, S Lacoste-Julien, D Sontag Advances in Neural Information Processing Systems, 532-540, 2015	47	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	28	2018
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology RJ Chen, RG Krishnan arXiv preprint arXiv:2203.00585, 2022	20	2022
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	15	2013
Neural pharmacodynamic state space modeling ZM Hussain, RG Krishnan, D Sontag International Conference on Machine Learning, 4500-4510, 2021	6	2021
Inference & Introspection in Deep Generative Models of Sparse Data RG Krishnan, M Hoffman Workshop for Advances in Approximate Bayesian Inference at NIPS 2016, 0	4
Hierarchical Optimal Transport for Comparing Histopathology Datasets A Yeaton, RG Krishnan, R Mieloszyk, D Alvarez-Melis, G Huynh arXiv preprint arXiv:2204.08324, 2022	3	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	3	2021
Max-margin learning with the Bayes factor. RG Krishnan, A Khandelwal, R Ranganath, D Sontag UAI, 896-905, 2018	3	2018
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	2	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	2	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	1	2022
Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help? SM Hu, S Horng, SJ Berkowitz, R Liao, RG Krishnan, LH Lehman, ... medRxiv, 2022.01. 15.22269345, 2022	1	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	1	2022
DuETT: Dual Event Time Transformer for Electronic Health Records A Labach, A Pokhrel, XS Huang, S Zuberi, SE Yi, M Volkovs, T Poutanen, ... arXiv preprint arXiv:2304.13017, 2023		2023

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