5 papers · 1 filter
Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
Zhenchao Sun, Shuai Ma, Ping Lu +1
Graph neural networks have been widely used in Boolean satisfiability (SAT) tasks to learn structural information from SAT formulas. The goal of these studies is to solve SAT insta…
DEvo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning
Ru Zhang, Renda Li, Ziyu Ma +4
Reinforcement learning (RL) has demonstrated potential for enhancing reasoning in large language models (LLMs). However, effective RL training, which requires medium-difficulty tra…
SetAD: Semi-Supervised Anomaly Learning in Contextual Sets
Jianling Gao, Chongyang Tao, Xuelian Lin +2
Semi-supervised anomaly detection (AD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring ind…
Dynamic Stratified Contrastive Learning with Upstream Augmentation for MILP Branching
Tongkai Lu, Shuai Ma, Chongyang Tao
Mixed Integer Linear Programming (MILP) is a fundamental class of NP-hard problems that has garnered significant attention from both academia and industry. The Branch-and-Bound (B\…
Empowering Targeted Neighborhood Search via Hyper Tour for Large-Scale TSP
Tongkai Lu, Shuai Ma, Chongyang Tao
Traveling Salesman Problem (TSP) is a classic NP-hard problem that has garnered significant attention from both academia and industry. While neural-based methods have shown promise…