21 citations · 30 across the 11 of their papers we have counts for
7 papers · 1 filter
A Survey of Weight Space Learning: Understanding, Representation, and Generation
Xiaolong Han, Zehong Wang, Bo Zhao +8
Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances…
Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
We introduce DP-FinDiff, a differentially private diffusion framework for synthesizing mixed-type tabular data. DP-FinDiff employs embedding-based representations for categorical f…
Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
Timur Sattarov, Marco Schreyer, Damian Borth
Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noi…
Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
Timur Sattarov, Marco Schreyer, Damian Borth
The increasing demand for privacy-preserving data analytics in various domains necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We int…
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs
Kristófer Reynisson, Marco Schreyer, Damian Borth
Credit card fraud has significant implications at both an individual and societal level, making effective prevention essential. Current methods rely heavily on feature engineering…
Sample Weight Estimation Using Meta-Updates for Online Continual Learning
Hamed Hemati, Damian Borth
The loss function plays an important role in optimizing the performance of a learning system. A crucial aspect of the loss function is the assignment of sample weights within a min…
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and…