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

Research

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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