Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is usually no standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are usually not diverse enough to draw sound conclusions. That is why recent work proposes a new methodology to generate a diverse set of instances by using an evolutionary algorithm. We can then analyze the resulting graphs and get key insights into which attributes are most related to algorithm performance. We can also fill observed gaps in the instance space in order to generate graphs with previously unseen combinations of features. This methodology is applied to the instance space of the Hamiltonian completion problem using two different solvers, namely the Concorde TSP Solver and a multi-start local search algorithm.
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
LIACS, Leiden University（莱顿大学信息科学研究中心）；Optimisation and Logistics, School of Computer and Mathematical Sciences, The University of Adelaide（计算机与数学科学学院优化与物流系，阿德莱德大学）；Mathematical Sciences, The University of Adelaide（数学科学，阿德莱德大学）
EvObj: Learning Evolving Object-centric Representations for 3D Instance Segmentation without Scene Supervision
Shenzhen Research Institute, The Hong Kong Polytechnic University（深圳研究 institute，香港理工大学）；vLAR Group, The Hong Kong Polytechnic University（vLAR 团队，香港理工大学）
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Peking University（北京大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；UC Berkeley（加州大学伯克利分校）
RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；College of Computer Science, Nankai University（南开大学计算机学院）；Department of Computer Science, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；School of Computer Science, Beihang University（北京航空航天大学计算机学院）；University of Notre Dame（圣母大学）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；JP Olive S&T Management LLC（JP Olive科技管理有限责任公司）
