Rahul G. Krishnan

According to our database, Rahul G. Krishnan authored at least 51 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection.

Tina Behrouzi, Sana Tonekaboni, Rahul G. Krishnan, Anna Goldenberg

CoRR, June, 2025

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning.

Vahid Balazadeh Meresht, Hamidreza Kamkari, Valentin Thomas, Benson Li, Junwei Ma, Jesse C. Cresswell, Rahul G. Krishnan

CoRR, June, 2025

Reliably detecting model failures in deployment without labels.

Viet Nguyen, Changjian Shui, Vijay Giri, Siddarth Arya, Amol Verma, Fahad Razak, Rahul G. Krishnan

CoRR, June, 2025

Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift.

Minh Nguyen Nhat To, Paul F. R. Wilson, Viet Nguyen, Mohamed Harmanani, Michael Cooper, Fahimeh Fooladgar, Purang Abolmaesumi, Parvin Mousavi, Rahul G. Krishnan

CoRR, May, 2025

Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking.

Chen-Hao Chao, Wei-Fang Sun, Hanwen Liang, Chun-Yi Lee, Rahul G. Krishnan

CoRR, May, 2025

Red Teaming Large Language Models for Healthcare.

Vahid Balazadeh Meresht, Michael Cooper, David Pellow, Atousa Assadi, Jennifer Bell, Jim Fackler, Gabriel Funingana, Spencer Gable-Cook, Anirudh Gangadhar, Abhishek Jaiswal, Sumanth Kaja, Christopher Khoury, Randy Lin, Kaden McKeen, Sara Naimimohasses, Khashayar Namdar, Aviraj Newatia, Allan Pang, Anshul Pattoo, Sameer Peesapati, Diana Prepelita, Bogdana Rakova, Saba Sadatamin, Rafael Schulman, Ajay Shah, Syed Azhar Shah, Syed Ahmar Shah, Babak Taati, Balagopal Unnikrishnan, Stephanie Williams, Rahul G. Krishnan

CoRR, May, 2025

Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge.

Mohammad R. Rezaei, Reza Saadati Fard, Jayson Parker, Rahul G. Krishnan, Milad Lankarany

CoRR, February, 2025

Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium.

Amin Adibi, Xu Cao, Zongliang Ji, Jivat Neet Kaur, Winston Chen, Elizabeth Healey, Brighton Nuwagira, Wenqian Ye, Geoffrey Woollard, Maxwell A. Xu, Hejie Cui, Johnny Xi, Trenton Chang, Vasiliki Bikia, Nicole Zhang, Ayush Noori, Yuan Xia, Md. Belal Hossain, Hanna A. Frank, Alina Peluso, Yuan Pu, Shannon Zejiang Shen, John Wu, Adibvafa Fallahpour, Sazan Mahbub, Ross Duncan, Yuwei Zhang, Yurui Cao, Zuheng Xu, Michael Craig, Rahul G. Krishnan, Rahmatollah Beheshti, James M. Rehg, Mohammad Ehsanul Karim, Megan Coffee, Leo Anthony Celi, Jason Alan Fries, Mohsen Sadatsafavi, Dennis L. Shung, Shannon K. McWeeney, Jessica Dafflon, Sarah Jabbour

CoRR, February, 2025

A generalizable 3D framework and model for self-supervised learning in medical imaging.

Tony Xu, Sepehr Hosseini, Chris Anderson, Anthony Rinaldi, Rahul G. Krishnan, Anne L. Martel, Maged Goubran

CoRR, January, 2025

Teaching LLMs How to Learn with Contextual Fine-Tuning.

Younwoo Choi, Muhammad Adil Asif, Ziwen Han, John Willes, Rahul G. Krishnan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders.

Zongliang Ji, Andre Carlos Kajdacsy-Balla Amaral, Anna Goldenberg, Rahul G. Krishnan

Proceedings of the Conference on Health, 2025

2024

Synthetic Vision: Training Vision-Language Models to Understand Physics.

Vahid Balazadeh Meresht, Mohammadmehdi Ataei, Hyunmin Cheong, Amir Hosein Khasahmadi, Rahul G. Krishnan

CoRR, 2024

Using Large Language Models for Expert Prior Elicitation in Predictive Modelling.

Alexander Capstick, Rahul G. Krishnan, Payam M. Barnaghi

CoRR, 2024

Learning Predictive Checklists with Probabilistic Logic Programming.

Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan

CoRR, 2024

Personalized Adaptation via In-Context Preference Learning.

Allison Lau, Younwoo Choi, Vahid Balazadeh Meresht, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan

CoRR, 2024

Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling.

Mohammad R. Rezaei, Rahul G. Krishnan, Milos R. Popovic, Milad Lankarany

CoRR, 2024

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning.

Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan

CoRR, 2024

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity.

Vahid Balazadeh Meresht, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

End-To-End Causal Effect Estimation from Unstructured Natural Language Data.

Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Predicting Long-Term Allograft Survival in Liver Transplant Recipients.

Xiang Gao, Michael Cooper, Maryam Naghibzadeh, Amirhossein Azhie, Mamatha Bhat, Rahul G. Krishnan

Proceedings of the Machine Learning for Healthcare Conference, 2024

NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild.

Rishit Dagli, Atsuhiro Hibi, Rahul G. Krishnan, Pascal N. Tyrrell

Proceedings of the Machine Learning for Healthcare Conference, 2024

InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation.

Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Geometric Explanation of the Likelihood OOD Detection Paradox.

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Automated screening of computed tomography using weakly supervised anomaly detection.

Atsuhiro Hibi, Michael D. Cusimano, Alexander Bilbily, Rahul G. Krishnan, Pascal N. Tyrrell

Int. J. Comput. Assist. Radiol. Surg., November, 2023

MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting.

Linfeng Du, Ji Xin, Alex Labach, Saba Zuberi, Maksims Volkovs, Rahul G. Krishnan

CoRR, 2023

OCDaf: Ordered Causal Discovery with Autoregressive Flows.

Hamidreza Kamkari, Vahid Zehtab, Vahid Balazadeh Meresht, Rahul G. Krishnan

CoRR, 2023

Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding.

Augustin Toma, Patrick R. Lawler, Jimmy Ba, Rahul G. Krishnan, Barry B. Rubin, Bo Wang

CoRR, 2023

Copula-based deep survival models for dependent censoring.

Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Structured Neural Networks for Density Estimation and Causal Inference.

Asic Q. Chen, Ruian Shi, Xiang Gao, Ricardo Baptista, Rahul G. Krishnan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DuETT: Dual Event Time Transformer for Electronic Health Records.

Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan

Proceedings of the Machine Learning for Healthcare Conference, 2023

A Learning Based Hypothesis Test for Harmful Covariate Shift.

Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections.

Edward De Brouwer, Rahul G. Krishnan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help?

Stephanie M. Hu, Steven Horng, Seth J. Berkowitz, Ruizhi Liao, Rahul G. Krishnan, Li-Wei H. Lehman, Roger G. Mark

Proceedings of the Computing in Cardiology, 2023

2022

Learning predictive checklists from continuous medical data.

Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan

CoRR, 2022

Mixture-of-experts VAEs can disregard variation in surjective multimodal data.

Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima

CoRR, 2022

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology.

Richard J. Chen, Rahul G. Krishnan

CoRR, 2022

Partial Identification of Treatment Effects with Implicit Generative Models.

Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding.

Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan

Proceedings of the Machine Learning for Healthcare Conference, 2022

Hierarchical Optimal Transport for Comparing Histopathology Datasets.

Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning.

Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Using time-series privileged information for provably efficient learning of prediction models.

Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Clustering Interval-Censored Time-Series for Disease Phenotyping.

Irene Y. Chen, Rahul G. Krishnan, David A. Sontag

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Clustering Left-Censored Multivariate Time-Series.

Irene Y. Chen, Rahul G. Krishnan, David A. Sontag

CoRR, 2021

Neural Pharmacodynamic State Space Modeling.

Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag

Proceedings of the 38th International Conference on Machine Learning, 2021

2018

Variational Autoencoders for Collaborative Filtering.

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

Max-margin learning with the Bayes factor.

Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics.

Eric P. Lehman, Rahul G. Krishnan, Xiaopeng Zhao, Roger G. Mark, Li-Wei H. Lehman

Proceedings of the Machine Learning for Healthcare Conference, 2018

On the challenges of learning with inference networks on sparse, high-dimensional data.

Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Structured Inference Networks for Nonlinear State Space Models.

Rahul G. Krishnan, Uri Shalit, David A. Sontag

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2015

Deep Kalman Filters.

Rahul G. Krishnan, Uri Shalit, David A. Sontag

CoRR, 2015

Barrier Frank-Wolfe for Marginal Inference.

Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015