learning-based methods
Learning-based methods encompass approaches that leverage data-driven learning principles to adapt and optimize algorithms or models, typically rooted in machine learning frameworks and methodologies.
- Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
- EDBench: Large-Scale Electron Density Data for Molecular Modeling
- FEAT: Free energy Estimators with Adaptive Transport
- OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization
- PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
- Reinforcement learning for one-shot DAG scheduling with comparability identification and dense reward
- RoME: Domain-Robust Mixture-of-Experts for MILP Solution Prediction across Domains
- STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
- Train on Pins and Test on Obstacles for Rectilinear Steiner Minimum Tree