Subhankar Ghosh

I am a graduate student in the Computer Science Department at Washington State University, Pullman, Where I am advised by Professor Yan Yan and Professor Jana Doppa.

Currently I am working on the uncertainty quantification domain, specifically in conformal prediction(CP). I also like to work in Generative models that combines language and vision.

Personal Email  /  Office Email  /  Google Scholar  /  Twitter  /  Github

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My accepted papers are highlighted.

Probabilistically Robust Conformal Prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan, Janardhan Rao Doppa​, Brian Jones
UAI, 2023   (Poster Presentation)
project page / PDF with Supplementary / GitHub

This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples.

NCP: Neighborhood Conformal Prediction
Subhankar Ghosh*, Taha Belkhouja*, Yan Yan, Janardhan Rao Doppa​
AAAI, 2023   (Oral Presentation)
project page / arXiv / GitHub / PPTX

We propose a novel algorithm and theory to improve the efficiency of prediction sets for conformal prediction method while maintaining the coverage guarantee.

A-CZSL: Adversarial Training of Variational Auto-encoders for Continual Zero-shot Learning
Subhankar Ghosh
IJCNN, 2021   (Oral Presentation)
IEEE / arXiv / Github code / video / PPTX

We propose a continual zero-shot learning model(A-CZSL) that is more suitable in real-case scenarios to address the issue that can learn sequentially and distinguish classes the model has not seen during training.

DVGR-CZSL:Dynamic vaes with generative replay for continual zero-shot learning
Subhankar Ghosh
CVPR, 2021   (findings)
CVPR workshop / arXiv / Github code

This paper proposes a novel continual zero-shot learning (DVGR-CZSL) model that grows in size with each task and uses generative replay to update itself with previously learned classes to avoid forgetting.

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