5 papers
Anomaly Detection via Mean Shift Density Enhancement
Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer +1
Unsupervised anomaly detection stands as an important problem in machine learning. Existing unsupervised anomaly detection algorithms rarely perform well across different anomaly t…
FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization
Sujan Chakraborty, Rahul Bordoloi, Anindya Sengupta +2
Graph-based learning is a cornerstone for analyzing structured data, with node classification as a central task. However, in many real-world graphs, nodes lack informative feature…
Dependency-aware synthetic tabular data generation
Chaithra Umesh, Kristian Schultz, Manjunath Mahendra +2
Synthetic tabular data is increasingly used in privacy-sensitive domains such as health care, but existing generative models often fail to preserve inter-attribute relationships. I…
Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
Rahul Bordoloi, Clémence Réda, Saptarshi Bej +1
Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification. However, imputation methods for classification might be time-consuming…
Convex space learning for tabular synthetic data generation
Manjunath Mahendra, Chaithra Umesh, Saptarshi Bej +2
Generating synthetic samples from the convex space of the minority class is a popular oversampling approach for imbalanced classification problems. Recently, deep-learning approach…