Barrier Frank-Wolfe for Marginal Inference. - Rahul G. Krishnan; Simon Lacoste-Julien; David A. Sontag (NIPS, 2015)

Deep Kalman Filters. - Rahul G. Krishnan; Uri Shalit; David A. Sontag (CoRR, 2015)

Structured Inference Networks for Nonlinear State Space Models. - Rahul G. Krishnan; Uri Shalit; David A. Sontag (AAAI, 2017)

On the challenges of learning with inference networks on sparse, high-dimensional data. - Rahul G. Krishnan; Dawen Liang; Matthew D. Hoffman (AISTATS, 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 (MLHC, 2018)

Max-margin learning with the Bayes factor. - Rahul G. Krishnan; Arjun Khandelwal; Rajesh Ranganath; David A. Sontag (UAI, 2018)

Variational Autoencoders for Collaborative Filtering. - Dawen Liang; Rahul G. Krishnan; Matthew D. Hoffman; Tony Jebara (WWW, 2018)

Neural Pharmacodynamic State Space Modeling. - Zeshan M. Hussain; Rahul G. Krishnan; David A. Sontag (ICML, 2021)

Clustering Left-Censored Multivariate Time-Series. - Irene Y. Chen; Rahul G. Krishnan; David A. Sontag (CoRR, 2021)

Clustering Interval-Censored Time-Series for Disease Phenotyping. - Irene Y. Chen; Rahul G. Krishnan; David A. Sontag (AAAI, 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 002 (AISTATS, 2022)

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning. - Richard J. Chen; Chengkuan Chen; Yicong Li 003; Tiffany Y. Chen; Andrew D. Trister; Rahul G. Krishnan; Faisal Mahmood 001 (CVPR, 2022)

Hierarchical Optimal Transport for Comparing Histopathology Datasets. - Anna Yeaton; Rahul G. Krishnan; Rebecca J. Mieloszyk; David Alvarez-Melis; Grace Huynh (MIDL, 2022)

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. - Weiming Ren; Ruijing Zeng; Tongzi Wu; Tianshu Zhu; Rahul G. Krishnan (MLHC, 2022)

Partial Identification of Treatment Effects with Implicit Generative Models. - Vahid Balazadeh Meresht; Vasilis Syrgkanis; Rahul G. Krishnan (NeurIPS, 2022)

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology. - Richard J. Chen; 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)

Learning predictive checklists from continuous medical data. - Yukti Makhija; Edward De Brouwer; Rahul G. Krishnan (CoRR, 2022)

Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help? - Stephanie M. Hu; Steven Horng; Seth J. Berkowitz; Ruizhi Liao 001; Rahul G. Krishnan; Li-Wei H. Lehman; Roger G. Mark (CinC, 2023)

Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. - Edward De Brouwer; Rahul G. Krishnan (ICLR, 2023)

A Learning Based Hypothesis Test for Harmful Covariate Shift. - Tom Ginsberg; Zhongyuan Liang; Rahul G. Krishnan (ICLR, 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 (MLHC, 2023)

Structured Neural Networks for Density Estimation and Causal Inference. - Asic Q. Chen; Ruian Shi; Xiang Gao 019; Ricardo Baptista; Rahul G. Krishnan (NeurIPS, 2023)

Copula-based deep survival models for dependent censoring. - Ali Hossein Gharari Foomani; Michael Cooper; Russell Greiner; Rahul G. Krishnan (UAI, 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)

OCDaf: Ordered Causal Discovery with Autoregressive Flows. - Hamidreza Kamkari; Vahid Zehtab; Vahid Balazadeh Meresht; Rahul G. Krishnan (CoRR, 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)

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., 2023)

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 (ICML, 2024)

InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation. - Jacob Yoke Hong Si; Wendy Yusi Cheng; Michael Cooper; Rahul G. Krishnan (ICML, 2024)

NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild. - Rishit Dagli; Atsuhiro Hibi; Rahul G. Krishnan; Pascal N. Tyrrell (MLHC, 2024)

Predicting Long-Term Allograft Survival in Liver Transplant Recipients. - Xiang Gao 019; Michael Cooper; Maryam Naghibzadeh; Amirhossein Azhie; Mamatha Bhat; Rahul G. Krishnan (MLHC, 2024)

End-To-End Causal Effect Estimation from Unstructured Natural Language Data. - Nikita Dhawan; Leonardo Cotta; Karen Ullrich; Rahul G. Krishnan; Chris J. Maddison (NeurIPS, 2024)

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity. - Vahid Balazadeh Meresht; Keertana Chidambaram; Viet Nguyen; Rahul G. Krishnan; Vasilis Syrgkanis (NeurIPS, 2024)

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning. - Zongliang Ji; Anna Goldenberg; 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)

Personalized Adaptation via In-Context Preference Learning. - Allison Lau; Younwoo Choi; Vahid Balazadeh Meresht; Keertana Chidambaram; Vasilis Syrgkanis; Rahul G. Krishnan (CoRR, 2024)

Learning Predictive Checklists with Probabilistic Logic Programming. - Yukti Makhija; Edward De Brouwer; 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)

Synthetic Vision: Training Vision-Language Models to Understand Physics. - Vahid Balazadeh Meresht; Mohammadmehdi Ataei; Hyunmin Cheong; Amir Hosein Khasahmadi; Rahul G. Krishnan (CoRR, 2024)

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 (CHIL, 2025)

Teaching LLMs How to Learn with Contextual Fine-Tuning. - Younwoo Choi; Muhammad Adil Asif; Ziwen Han; John Willes; Rahul G. Krishnan (ICLR, 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, 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; et al. (CoRR, 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, 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; et al. (CoRR, 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, 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, 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, 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, 2025)

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection. - Tina Behrouzi; Sana Tonekaboni; Rahul G. Krishnan; Anna Goldenberg (CoRR, 2025)