Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks. - Jay B. Patel; Ken Chang; Katharina Hoebel; Mishka Gidwani; Nishanth Thumbavanam Arun; Sharut Gupta; Mehak Aggarwal; Praveer Singh; Bruce R. Rosen; Elizabeth R. Gerstner; Jayashree Kalpathy-Cramer (BrainLes@MICCAI (2), 2020)

Federated Learning for Breast Density Classification: A Real-World Implementation. - Holger R. Roth; Ken Chang; Praveer Singh; Nir Neumark; Wenqi Li 001; Vikash Gupta; Sharut Gupta; Liangqiong Qu; Alvin Ihsani; Bernardo C. Bizzo; Yuhong Wen; Varun Buch; et al. (DART/DCL@MICCAI, 2020)

Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. - Nishanth Thumbavanam Arun; Nathan Gaw; Praveer Singh; Ken Chang; Mehak Aggarwal; Bryan Chen; Katharina Hoebel; Sharut Gupta; Jay B. Patel; Mishka Gidwani; Julius Adebayo; Matthew D. Li; et al. (CoRR, 2020)

Towards Trainable Saliency Maps in Medical Imaging. - Mehak Aggarwal; Nishanth Thumbavanam Arun; Sharut Gupta; Ashwin Vaswani; Bryan Chen; Matthew D. Li; Ken Chang; Jay B. Patel; Katherine Höbel; Mishka Gidwani; Jayashree Kalpathy-Cramer; Praveer Singh (CoRR, 2020)

The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions. - Sharut Gupta; Praveer Singh; Ken Chang; Mehak Aggarwal; Nishanth Thumbavanam Arun; Liangqiong Qu; Katharina Hoebel; Jay B. Patel; Mishka Gidwani; Ashwin Vaswani; Daniel L. Rubin; Jayashree Kalpathy-Cramer (CoRR, 2020)

Addressing catastrophic forgetting for medical domain expansion. - Sharut Gupta; Praveer Singh; Ken Chang; Liangqiong Qu; Mehak Aggarwal; Nishanth Thumbavanam Arun; Ashwin Vaswani; Shruti Raghavan; Vibha Agarwal; Mishka Gidwani; Katharina Hoebel; Jay B. Patel; et al. (CoRR, 2021)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. - Raghav Mehta; Angelos Filos; Ujjwal Baid; Chiharu Sako; Richard McKinley; Michael Rebsamen; Katrin Dätwyler; Raphael Meier; Piotr Radojewski; Gowtham Krishnan Murugesan; Sahil S. Nalawade; Chandan Ganesh; et al. (CoRR, 2021)

Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning. - Mamoudou Sangaré; Sharut Gupta; Samia Bouzefrane 001; Soumya Banerjee 002; Paul Mühlethaler (Expert Syst. Appl., 2021)

AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation. - Farshid Varno; Marzie Saghayi; Laya Rafiee Sevyeri; Sharut Gupta; Stan Matwin; Mohammad Havaei (ECCV (23), 2022)

Profiling Fake News Spreaders on Twitter: A Clickbait and Linguistic Feature Based Scheme. - Raksha Agarwal; Sharut Gupta; Niladri Chatterjee (NLDB, 2022)

Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation. - Farshid Varno; Marzie Saghayi; Laya Rafiee; Sharut Gupta; Stan Matwin; Mohammad Havaei (CoRR, 2022)

FL Games: A Federated Learning Framework for Distribution Shifts. - Sharut Gupta; Kartik Ahuja; Mohammad Havaei; Niladri Chatterjee; Yoshua Bengio (CoRR, 2022)

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. - Sharut Gupta; Joshua Robinson 001; Derek Lim; Soledad Villar; Stefanie Jegelka (ICLR, 2024)

Context is Environment. - Sharut Gupta; Stefanie Jegelka; David Lopez-Paz; Kartik Ahuja (ICLR, 2024)

Removing Biases from Molecular Representations via Information Maximization. - Chenyu Wang 003; Sharut Gupta; Caroline Uhler; Tommi S. Jaakkola (ICLR, 2024)

Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion. - Sharut Gupta; Ken Chang; Liangqiong Qu; Aakanksha Rana; Syed Rakin Ahmed; Mehak Aggarwal; Nishanth Thumbavanam Arun; Ashwin Vaswani; Shruti Raghavan; Vibha Agarwal; Mishka Gidwani; Katharina Hoebel; et al. (AIPAD/PILM@MICCAI, 2024)

Understanding the Role of Equivariance in Self-supervised Learning. - Yifei Wang 001; Kaiwen Hu; Sharut Gupta; Ziyu Ye; Yisen Wang 001; Stefanie Jegelka (NeurIPS, 2024)

In-Context Symmetries: Self-Supervised Learning through Contextual World Models. - Sharut Gupta; Chenyu Wang 003; Yifei Wang 001; Tommi S. Jaakkola; Stefanie Jegelka (NeurIPS, 2024)

An Information Criterion for Controlled Disentanglement of Multimodal Data. - Chenyu Wang 003; Sharut Gupta; Xinyi Zhang; Sana Tonekaboni; Stefanie Jegelka; Tommi S. Jaakkola; Caroline Uhler (ICLR, 2025)

Sequential-Parallel Duality in Prefix Scannable Models. - Morris Yau; Sharut Gupta; Valerie Engelmayer; Kazuki Irie; Stefanie Jegelka; Jacob Andreas (CoRR, 2025)

Learning Diffusion Models with Flexible Representation Guidance. - Chenyu Wang 003; Cai Zhou; Sharut Gupta; Zongyu Lin; Stefanie Jegelka; Stephen Bates; Tommi S. Jaakkola (CoRR, 2025)