Shubham Sharma

According to our database, Shubham Sharma authored at least 8 papers between 2019 and 2023.

Collaborative distances:

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

2023

FEAMOE: Fair, Explainable and Adaptive Mixture of Experts.

Shubham Sharma, Jette Henderson, Joydeep Ghosh

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022

FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations.

Shubham Sharma, Alan H. Gee, Jette Henderson, Joydeep Ghosh

CoRR, 2022

2021

FaiR-N: Fair and Robust Neural Networks for Structured Data.

Shubham Sharma, Alan H. Gee, David Paydarfar, Joydeep Ghosh

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Certifai: A Toolkit for Building Trust in AI Systems.

Jette Henderson, Shubham Sharma, Alan H. Gee, Valeri Alexiev, Steve Draper, Carlos Marin, Yessel Hinojosa, Christine Draper, Michael Perng, Luis Aguirre, Michael Li, Sara Rouhani, Shorya Consul, Susan Michalski, Akarsh Prasad, Mayank Chutani, Aditya Kumar, Shahzad Alam, Prajna Kandarpa, Binnu Jesudasan, Colton Lee, Michael Criscolo, Sinead Williamson, Matt Sanchez, Joydeep Ghosh

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Explainable machine learning in deployment.

Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley

Proceedings of the FAT* '20: Conference on Fairness, 2020

Data Augmentation for Discrimination Prevention and Bias Disambiguation.

Shubham Sharma, Yunfeng Zhang, Jesús M. Ríos Aliaga, Djallel Bouneffouf, Vinod Muthusamy, Kush R. Varshney

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models.

Shubham Sharma, Jette Henderson, Joydeep Ghosh

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019

CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models.

Shubham Sharma, Jette Henderson, Joydeep Ghosh

CoRR, 2019