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ORCIDRahul G. Krishnan
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2020 – today
- 2024
[c21]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. ICML 2024- [c20]Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan:
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation. ICML 2024 - [i34]Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan:
Measurement Scheduling for ICU Patients with Offline Reinforcement Learning. CoRR abs/2402.07344 (2024) - [i33]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 abs/2403.18910 (2024) - [i32]Vahid Balazadeh Meresht, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis:
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity. CoRR abs/2404.07266 (2024) - [i31]Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan:
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation. CoRR abs/2406.00426 (2024) - [i30]Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison:
End-To-End Causal Effect Estimation from Unstructured Natural Language Data. CoRR abs/2407.07018 (2024) - [i29]Xiang Gao, Michael Cooper, Maryam Naghibzadeh, Amirhossein Azhie, Mamatha Bhat, Rahul G. Krishnan:
Predicting Long-Term Allograft Survival in Liver Transplant Recipients. CoRR abs/2408.05437 (2024) - [i28]Rishit Dagli, Atsuhiro Hibi, Rahul G. Krishnan, Pascal N. Tyrrell:
NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild. CoRR abs/2408.10258 (2024) - [i27]Mohammad R. Rezaei, Rahul G. Krishnan, Milos R. Popovic, Milad Lankarany:
Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling. CoRR abs/2409.14599 (2024) - [i26]Allison Lau, Younwoo Choi, Vahid Balazadeh Meresht, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan:
Personalized Adaptation via In-Context Preference Learning. CoRR abs/2410.14001 (2024) - [i25]Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan:
Learning Predictive Checklists with Probabilistic Logic Programming. CoRR abs/2411.16790 (2024) - [i24]Alexander Capstick, Rahul G. Krishnan, Payam M. Barnaghi:
Using Large Language Models for Expert Prior Elicitation in Predictive Modelling. CoRR abs/2411.17284 (2024) - 2023
- [j1]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): 2001-2012 (2023) - [c19]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: 1-4 - [c18]Edward De Brouwer, Rahul G. Krishnan:
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. ICLR 2023 - [c17]Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan:
A Learning Based Hypothesis Test for Harmful Covariate Shift. ICLR 2023 - [c16]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: 403-422 - [c15]Asic Q. Chen, Ruian Shi, Xiang Gao, Ricardo Baptista, Rahul G. Krishnan:
Structured Neural Networks for Density Estimation and Causal Inference. NeurIPS 2023 - [c14]Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan:
Copula-based deep survival models for dependent censoring. UAI 2023: 669-680 - [i23]Edward De Brouwer, Rahul G. Krishnan:
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. CoRR abs/2303.01841 (2023) - [i22]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 abs/2304.13017 (2023) - [i21]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 abs/2305.12031 (2023) - [i20]Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan:
Copula-Based Deep Survival Models for Dependent Censoring. CoRR abs/2306.11912 (2023) - [i19]Hamidreza Kamkari, Vahid Zehtab, Vahid Balazadeh Meresht, Rahul G. Krishnan:
OCDaf: Ordered Causal Discovery with Autoregressive Flows. CoRR abs/2308.07480 (2023) - [i18]Asic Q. Chen, Ruian Shi, Xiang Gao, Ricardo Baptista, Rahul G. Krishnan:
Structured Neural Networks for Density Estimation and Causal Inference. CoRR abs/2311.02221 (2023) - [i17]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 abs/2311.18780 (2023) - 2022
- [c13]Irene Y. Chen, Rahul G. Krishnan, David A. Sontag:
Clustering Interval-Censored Time-Series for Disease Phenotyping. AAAI 2022: 6211-6221 - [c12]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: 5459-5484 - [c11]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: 16123-16134 - [c10]Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh:
Hierarchical Optimal Transport for Comparing Histopathology Datasets. MIDL 2022: 1459-1469 - [c9]Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan:
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. MLHC 2022: 198-223 - [c8]Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan:
Partial Identification of Treatment Effects with Implicit Generative Models. NeurIPS 2022 - [i16]Richard J. Chen, Rahul G. Krishnan:
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology. CoRR abs/2203.00585 (2022) - [i15]Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima:
Mixture-of-experts VAEs can disregard variation in surjective multimodal data. CoRR abs/2204.05229 (2022) - [i14]Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh:
Hierarchical Optimal Transport for Comparing Histopathology Datasets. CoRR abs/2204.08324 (2022) - [i13]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 abs/2206.02647 (2022) - [i12]Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan:
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. CoRR abs/2208.02301 (2022) - [i11]Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan:
Partial Identification of Treatment Effects with Implicit Generative Models. CoRR abs/2210.08139 (2022) - [i10]Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan:
Learning predictive checklists from continuous medical data. CoRR abs/2211.07076 (2022) - [i9]Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan:
A Learning Based Hypothesis Test for Harmful Covariate Shift. CoRR abs/2212.02742 (2022) - 2021
- [c7]Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag:
Neural Pharmacodynamic State Space Modeling. ICML 2021: 4500-4510 - [i8]Irene Y. Chen, Rahul G. Krishnan, David A. Sontag:
Clustering Left-Censored Multivariate Time-Series. CoRR abs/2102.07005 (2021) - [i7]Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag:
Neural Pharmacodynamic State Space Modeling. CoRR abs/2102.11218 (2021) - [i6]Rickard Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik D. Johansson:
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. CoRR abs/2110.14993 (2021)
2010 – 2019
- 2018
- [c6]Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. AISTATS 2018: 143-151 - [c5]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: 571-586 - [c4]Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag:
Max-margin learning with the Bayes factor. UAI 2018: 896-905 - [c3]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. WWW 2018: 689-698 - [i5]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. CoRR abs/1802.05814 (2018) - 2017
- [c2]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Structured Inference Networks for Nonlinear State Space Models. AAAI 2017: 2101-2109 - [i4]Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. CoRR abs/1710.06085 (2017) - 2016
- [i3]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Structured Inference Networks for Nonlinear State Space Models. CoRR abs/1609.09869 (2016) - 2015
- [c1]Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. NIPS 2015: 532-540 - [i2]Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. CoRR abs/1511.02124 (2015) - [i1]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Deep Kalman Filters. CoRR abs/1511.05121 (2015)
Coauthor Index
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