Author

Jialiang Li

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#machine learning Nov 2025

Neural Tractability via Structure: Learning-Augmented Algorithms for Graph Combinatorial Optimization

The proposed Neural Fixed-Parameter Tractable (N-FPT), a neural-model-agnostic framework that uses parameterized algorithms to improve neural solutions without retraining and guide learning, proves this guarantee and global optimality under perfect advice.

Jialiang Li, Wei-Tong Chen, Ming-Yu Guo · arXiv.org · 0 citations

Conference Open access Aug 2026

Taming Treewidth DP with Modulators: A General Booster for Graph Heuristics

This work applies TDP to enhance algorithms across diverse paradigms, including evolutionary search, greedy heuristics, and graph-neural-network-based heuristics, and shows that TDP significantly improves performance over the original methods.

Jialiang Li, Aneta Neumann, Frank Neumann et al. · Proceedings of the Thirty-Fi... · 1 citation