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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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Portfolio item number 2
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publications
Low rank label subspace transformation for multi-label learning with missing labels
Published in Information Sciences, 2022
This paper proposes a unified framework, LRMML, that recovers missing labels in multi-label datasets by jointly leveraging auxiliary labels, low-rank constraints, and inter-label subspace separation to capture both local and global label correlations.
Recommended citation: Kumar, Sanjay, and Reshma Rastogi. "Low rank label subspace transformation for multi-label learning with missing labels. "Information Sciences (2022).
Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels
Published in Neural Processing Letters, 2022
This paper proposes CIMML, a class imbalance-aware multi-label learning method for missing labels that estimates label weights based on observed, absent, and unobserved frequencies, and incorporates these into a weighted loss guided by auxiliary label correlations for improved label completion and classification.
Recommended citation: Rastogi, Reshma, and Sanjay Kumar. "Discriminatory label-specific weights for multi-label learning with missing labels. "Neural Processing Letters (2023).
Multi-label learning with missing labels using sparse global structure for label-specific features
Published in Applied Intelligence, 2023
This paper proposes a multi-label learning method for missing labels that identifies label-specific features constrained by a sparse global structure, enabling effective learning in high-dimensional spaces while leveraging supplementary label correlations for improved label recovery.
Recommended citation: Kumar, Sanjay, Nadira Ahmadi, and Reshma Rastogi. "Multi-label learning with missing labels using sparse global structure for label-specific features. "Applied Intelligence (2023).
Auxiliary label embedding for multi-label learning with missing labels
Published in Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, 2023
This paper proposes a correlation embedding-based approach for multi-label learning with incomplete labels that learns an auxiliary label matrix and preserves label correlation structure in model coefficients to recover missing labels and improve classification performance.
Recommended citation: Kumar, Sanjay, and Reshma Rastogi. "Auxiliary label embedding for multi-label learning with missing labels." In Computer Vision and Machine Intelligence: Proceedings of CVMI 2022
Addressing Multi-Label Learning with Missing Labels via Feature Relevance guided Scaled Model Coefficients
Published in International Conference on Pattern Recognition, 2024
This paper proposes a multi-label learning method for missing labels that employs modified ℓ₂-norm regularization to encode feature relevance with global sparsity, incorporates label correlation learning, and uses squared hinge loss for robust classification.
Recommended citation: Kumar, Sanjay, and Reshma Rastogi. "Addressing Multi-Label Learning with Missing Labels via Feature Relevance guided Scaled Model Coefficients." In International Conference on Pattern Recognition, Springer Nature Switzerlan, 2024
GBSVR: Granular Ball Support Vector Regression
Published in arXiv preprint, 2025
This paper proposes GBSVR, a granular ball-based support vector regression method that reduces computational complexity and improves robustness to outliers by approximating data with granular balls and introducing a discretization strategy for continuous features.
Recommended citation: Rastogi, Reshma, Ankush Bisht, Sanjay Kumar, and Suresh Chandra. "GBSVR: Granular Ball Support Vector Regression. "arXiv preprint arXiv (2025).
talks
ALEML, Conference Proceeding CVMI
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ALEML paper presentation
Applied Machine Learning with Real-World Economic Data
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Conducted a hands-on session demonstrating the application of selected Machine Learning algorithms to real-world economic data.
Classical/Deep learning approaches for diagnosing Neurodegenerative Diseases.
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Live demonstration of selected Machine Learning and Deep Learning Algorithms on Medical data including MRI images.
Code and Data with Python
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Conducted a workshop on data handling and programming with Python at Far Western University, Nepal.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.