Sharut Gupta

According to our database, Sharut Gupta authored at least 19 papers between 2020 and 2024.

Collaborative distances:

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

2024

Understanding the Role of Equivariance in Self-supervised Learning.

Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka

CoRR, 2024

An Information Criterion for Controlled Disentanglement of Multimodal Data.

Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler

CoRR, 2024

In-Context Symmetries: Self-Supervised Learning through Contextual World Models.

Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka

CoRR, 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, Jay B. Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer, Praveer Singh

Proceedings of the Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine, 2024

Removing Biases from Molecular Representations via Information Maximization.

Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Context is Environment.

Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning.

Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022

FL Games: A Federated Learning Framework for Distribution Shifts.

Sharut Gupta, Kartik Ahuja, Mohammad Havaei, Niladri Chatterjee, Yoshua Bengio

CoRR, 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

Profiling Fake News Spreaders on Twitter: A Clickbait and Linguistic Feature Based Scheme.

Raksha Agarwal, Sharut Gupta, Niladri Chatterjee

Proceedings of the Natural Language Processing and Information Systems, 2022

AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation.

Farshid Varno, Marzie Saghayi, Laya Rafiee Sevyeri, Sharut Gupta, Stan Matwin, Mohammad Havaei

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning.

Mamoudou Sangaré, Sharut Gupta, Samia Bouzefrane, Soumya Banerjee, Paul Mühlethaler

Expert Syst. Appl., 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, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Verónica Vilaplana, Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang, Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Lladó, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer, Nisarg A. Shah, Sanjay N. Talbar, Marc-André Weber, Abhishek Mahajan, András Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel S. Marcus, Aikaterini Kotrotsou, Rivka Colen, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Bjoern H. Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

CoRR, 2021

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, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer

CoRR, 2021

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

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

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, Jayashree Kalpathy-Cramer

CoRR, 2020

Federated Learning for Breast Density Classification: A Real-World Implementation.

Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer

Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020

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

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020