2 citations · 2 across the 3 of their papers we have counts for
6 papers
Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes
Zhaoxuan Li, Jiale Yang, Yifei Lu +1
Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio. Thi…
Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking
Jiabao Brad Wang, Siyuan Cao, Hongxuan Wu +2
Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes. MolAS is a lightweig…
GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization
Jiabao Brad Wang, Xiang Shi, Yiliang Yuan +1
Automated algorithm selection for continuous black-box optimization depends on representing problem information under limited probing and selecting solvers under heavy-tailed perfo…
Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization
Yiliang Yuan, Xiang Shi, Mustafa Misir
The present paper introduces a new representation-driven approach to per-instance algorithm selection, applied to black-box optimization, for automatically choosing the most promis…
MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
Siyuan Cao, Hongxuan Wu, Jiabao Brad Wang +2
Molecular docking is a core tool in drug discovery for predicting ligand-target interactions. Despite the availability of diverse search-based and machine learning approaches, no s…
GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
Yiliang Yuan, Mustafa Misir
Molecular docking is a major element in drug discovery and design. It enables the prediction of ligand-protein interactions by simulating the binding of small molecules to proteins…