Rahul G. Krishnan

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 45

Top Topics

Reinforcement Learning

Causal Inference

Top Venues

Publications

Jacob Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation. CoRR (2024)
Vahid Balazadeh Meresht, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity. CoRR (2024)
Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
Measurement Scheduling for ICU Patients with Offline Reinforcement Learning. CoRR (2024)
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison
End-To-End Causal Effect Estimation from Unstructured Natural Language Data. CoRR (2024)
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
A Geometric Explanation of the Likelihood OOD Detection Paradox. CoRR (2024)
Augustin Toma, Patrick R. Lawler, Jimmy Ba, Rahul G. Krishnan, Barry B. Rubin, Bo Wang
Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding. CoRR (2023)
Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan
A Learning Based Hypothesis Test for Harmful Covariate Shift. ICLR (2023)
Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan
DuETT: Dual Event Time Transformer for Electronic Health Records. MLHC (2023)
Hamidreza Kamkari, Vahid Zehtab, Vahid Balazadeh Meresht, Rahul G. Krishnan
OCDaf: Ordered Causal Discovery with Autoregressive Flows. CoRR (2023)
Linfeng Du, Ji Xin, Alex Labach, Saba Zuberi, Maksims Volkovs, Rahul G. Krishnan
MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting. CoRR (2023)
Edward De Brouwer, Rahul G. Krishnan
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. CoRR (2023)
Edward De Brouwer, Rahul G. Krishnan
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. ICLR (2023)
Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan
DuETT: Dual Event Time Transformer for Electronic Health Records. CoRR (2023)
Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan
Copula-based deep survival models for dependent censoring. UAI (2023)
Asic Q. Chen, Ruian Shi, Xiang Gao, Ricardo Baptista, Rahul G. Krishnan
Structured Neural Networks for Density Estimation and Causal Inference. CoRR (2023)
Stephanie M. Hu, Steven Horng, Seth J. Berkowitz, Ruizhi Liao, Rahul G. Krishnan, Li-Wei H. Lehman, Roger G. Mark
Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help? CinC (2023)
Atsuhiro Hibi, Michael D. Cusimano, Alexander Bilbily, Rahul G. Krishnan, Pascal N. Tyrrell
Automated screening of computed tomography using weakly supervised anomaly detection. Int. J. Comput. Assist. Radiol. Surg. 18 (11) (2023)
Asic Q. Chen, Ruian Shi, Xiang Gao, Ricardo Baptista, Rahul G. Krishnan
Structured Neural Networks for Density Estimation and Causal Inference. NeurIPS (2023)
Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan
Copula-Based Deep Survival Models for Dependent Censoring. CoRR (2023)
Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh
Hierarchical Optimal Transport for Comparing Histopathology Datasets. CoRR (2022)
Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan
Partial Identification of Treatment Effects with Implicit Generative Models. NeurIPS (2022)
Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson
Using time-series privileged information for provably efficient learning of prediction models. AISTATS (2022)
Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning. CoRR (2022)
Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. MLHC (2022)
Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan
Partial Identification of Treatment Effects with Implicit Generative Models. CoRR (2022)
Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan
Learning predictive checklists from continuous medical data. CoRR (2022)
Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. CoRR (2022)
Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning. CVPR (2022)
Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan
A Learning Based Hypothesis Test for Harmful Covariate Shift. CoRR (2022)
Irene Y. Chen, Rahul G. Krishnan, David A. Sontag
Clustering Interval-Censored Time-Series for Disease Phenotyping. AAAI (2022)
Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh
Hierarchical Optimal Transport for Comparing Histopathology Datasets. MIDL (2022)
Richard J. Chen, Rahul G. Krishnan
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology. CoRR (2022)
Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima
Mixture-of-experts VAEs can disregard variation in surjective multimodal data. CoRR (2022)
Zeshan Hussain, Rahul G. Krishnan, David A. Sontag
Neural Pharmacodynamic State Space Modeling. CoRR (2021)
Zeshan Hussain, Rahul G. Krishnan, David A. Sontag
Neural Pharmacodynamic State Space Modeling. ICML (2021)
Irene Y. Chen, Rahul G. Krishnan, David A. Sontag
Clustering Left-Censored Multivariate Time-Series. CoRR (2021)
Rickard Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik D. Johansson
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. CoRR (2021)
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman
On the challenges of learning with inference networks on sparse, high-dimensional data. AISTATS (2018)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Variational Autoencoders for Collaborative Filtering. CoRR (2018)
Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag
Max-margin learning with the Bayes factor. UAI (2018)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Variational Autoencoders for Collaborative Filtering. WWW (2018)
Eric P. Lehman, Rahul G. Krishnan, Xiaopeng Zhao, Roger G. Mark, Li-Wei H. Lehman
Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics. MLHC (2018)
Rahul G. Krishnan, Uri Shalit, David A. Sontag
Structured Inference Networks for Nonlinear State Space Models. AAAI (2017)
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman
On the challenges of learning with inference networks on sparse, high-dimensional data. CoRR (2017)
Rahul G. Krishnan, Uri Shalit, David A. Sontag
Structured Inference Networks for Nonlinear State Space Models. CoRR (2016)
Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag
Barrier Frank-Wolfe for Marginal Inference. NIPS (2015)
Rahul G. Krishnan, Uri Shalit, David A. Sontag
Deep Kalman Filters. CoRR (2015)
Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag
Barrier Frank-Wolfe for Marginal Inference. CoRR (2015)