3 papers
cs.LG2026
Counterfactual Residual Data Augmentation for Regression
Hossein Mohebbi, Oliver Schulte, Ke Li +1
Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augme…
cs.LG2022
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf +1
Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties s…
cs.LG2021
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte +3
Recent work on graph generative models has made remarkable progress towards generating increasingly realistic graphs, as measured by global graph features such as degree distributi…