arXiv Machine Learning

Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks

arXiv:2602. 14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics.

arXiv AI

Jun 26

Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture

arXiv:2508. 08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances.

By Xiang Li, Shanshan Wang, Chenglong Xiao

agents benchmarks

arXiv Machine Learning

Jul 22

Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes

arXiv:2607. 18632v1 Announce Type: new Abstract: Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio.

By Zhaoxuan Li, Jiale Yang, Yifei Lu, Mustafa Misir

arXiv Machine Learning

Jun 17

Amortizing Maximum Inner Product Search with Learned Support Functions

arXiv:2603. 08001v2 Announce Type: replace Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a given query.

By Theo X. Olausson, Jo\~ao Monteiro, Michal Klein, Marco Cuturi

rag benchmarks

arXiv Machine Learning

Jul 2

Amortized Maximum Inner Product Search with Learned Support Functions

arXiv:2603. 08001v3 Announce Type: replace Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a given query.

By Theo X. Olausson, Jo\~ao Monteiro, Michal Klein, Marco Cuturi

rag benchmarks

arXiv AI

Jul 14

Transformer-Guided Swarm Intelligence for Frugal Neural Architecture Search

arXiv:2607. 11826v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in thousands of GPU-days.

By Romain Amigon

llms reinforcement-learning efficiency

Hugging Face Trending Papers

Jul 21

Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes

Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio. This is particularly relevant to the Traveling Salesman Problem (TSP), where solver performance is strongly instance-dependent.

arXiv AI

Jun 9

Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits

arXiv:2606. 07615v1 Announce Type: cross Abstract: Deep neural networks often contain redundant hidden units.

By Salem Ameen, Sunil Vadera

reinforcement-learning efficiency benchmarks

arXiv AI

Jun 4

Learning Empirically Admissible Neural Heuristics for Combinatorial Search

arXiv:2606. 04860v1 Announce Type: cross Abstract: Finding optimal solution paths for combinatorial puzzles like the Rubik's Cube, sliding tile puzzles, and Lights Out remains a classical challenge in artificial intelligence.

By Siddharth Sahay

reinforcement-learning safety

arXiv AI

Jun 9

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives

arXiv:2601. 01665v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs).

By Wei Liu, Yaoxin Wu, Yingqian Zhang, Thomas B\"ack, Yingjie Fan

reinforcement-learning safety

arXiv Machine Learning

Jun 2

Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts

arXiv:2605. 09382v2 Announce Type: replace Abstract: The Linear Assignment Problem is a fundamental combinatorial optimization task where classical exact solvers ensure optimality but suffer from an $\mathcal{O}(N^{3})$ bottleneck, while recent neural approximations struggle with scalability and exactness.

By Ilay Yavlovich, Jad Agbaria, Muhamed Mhamed, Nir Weinberger, Jose Yallouz

arXiv Machine Learning

Jul 7

Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration

arXiv:2606. 12913v2 Announce Type: replace Abstract: The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning~(DP) methods which retain only a subset of informative samples to reduce training cost.

By Dongyue Wu, Zilin Guo, Xiaoyu Li, Jiajia Liu, Jingdong Chen, Nong Sang, Changxin Gao

efficiency

arXiv Machine Learning

6d ago

AutoGrable: What Is a Good Graph for a Table?

arXiv:2608. 11431v1 Announce Type: new Abstract: Graph learning presupposes a graph, and tables and relational databases do not come with one.

By Tamara Cucumides, Floris Geerts

safety

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