Full Professor, University of Illinois at Urbana-Champaign
3 papers at NeurIPS 2025
We propose a model-free data selection method to address domain adaptation problem for graph classification.
We propose Hephaestus, a generative self-reinforcing framework for solving the QoSD problem on large networks with nonlinear edge-weight functions, outperforming prior combinatorial and ML-based methods
We introduce CLIMB, a comprehensive benchmark and empirical study of 29 class-imbalanced learning methods on 73 real-world tabular datasets, revealing key insights into method performance, efficiency, and robustness.
