5 citations · 5 across the 7 of their papers we have counts for
9 papers
HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs
Megha P, Harshit Kumar, Srajan Agarwal +3
Self-supervised hypergraph representation learning can produce informative node embeddings, but existing methods often require deep encoders trained for hundreds of epochs, making…
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…
Preserving logical and functional dependencies in synthetic tabular data
Chaithra Umesh, Kristian Schultz, Manjunath Mahendra +2
Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthet…
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…
Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
Rahul Bordoloi, Clémence Réda, Orell Trautmann +2
Functional linear discriminant analysis (FLDA) is a powerful tool that extends LDA-mediated multiclass classification and dimension reduction to univariate time-series functions. H…
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets
Kristian Schultz, Saptarshi Bej, Waldemar Hahn +3
Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to…