Rahul G. Krishnan

According to our database, Rahul G. Krishnan authored at least 30 papers between 2015 and 2024.

Collaborative distances:

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

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

CoRR, 2024

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning.

Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan

CoRR, 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