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
Faisal MahmoodAssociate Professor, Harvard UniversityVerified email at fas.harvard.edu
Michael CooperPhD Student, University of TorontoVerified email at cs.toronto.edu
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Zeshan M HussainMD-PhD Student, Harvard Medical School, MITVerified email at mit.edu
Vasilis SyrgkanisAssistant Professor, Stanford UniversityVerified email at stanford.edu
Vahid BalazadehPhD in Computer ScienceVerified email at cs.toronto.edu
Narges RazavianNew York University Medical CenterVerified email at nyumc.org
Pascal N. TyrrellDepartment of Medical Imaging - University of TorontoVerified email at tyrrell4innovation.ca
Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Bo WangChief AI Scientist,UHN; CIFAR AI Chair, Vector Institute; Assistant Professor, U of TorontoVerified email at vectorinstitute.ai
Tom GinsbergGraduate Student, Univeristy of TorontoVerified email at cs.toronto.edu
Edward De BrouwerYale UniversityVerified email at esat.kuleuven.be
David Alvarez-MelisMicrosoft Research & Harvard UniversityVerified email at seas.harvard.edu
Grace HuynhMITVerified email at mit.edu

Rahul G. Krishnan

University of Toronto

Verified email at cs.toronto.edu - Homepage

Machine Learning Artificial Intelligence Healthcare Probabilistic models


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	1074	2018
Structured Inference Networks for Nonlinear State Space Models RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1609.09869, 2016	473	2016
Deep Kalman Filters RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1511.05121, 2015	388	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	178	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	85	2018
Barrier Frank-Wolfe for marginal inference RG Krishnan, S Lacoste-Julien, D Sontag Advances in Neural Information Processing Systems, 532-540, 2015	46	2015
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology RJ Chen, RG Krishnan arXiv preprint arXiv:2203.00585, 2022	42	2022
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	30	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	19	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	10*	2022
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	9	2023
Neural pharmacodynamic state space modeling ZM Hussain, RG Krishnan, D Sontag International Conference on Machine Learning, 4500-4510, 2021	9	2021
A Learning Based Hypothesis Test for Harmful Covariate Shift T Ginsberg, Z Liang, RG Krishnan arXiv preprint arXiv:2212.02742, 2022	7	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	5	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	4	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
Machine learning in computational histopathology: Challenges and opportunities M Cooper, Z Ji, RG Krishnan Genes, Chromosomes and Cancer, 2023	3	2023
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	3	2022
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 Machine Learning for Healthcare Conference, 198-223, 2022	2	2022

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