Research map / Probabilistic and scientific modeling / Bayesian optimization
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization (ICML 2024 · similarity 0.89)
- Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization (AAAI 2019 · similarity 0.89)
- Bayesian Optimization of Combinatorial Structures (ICML 2018 · similarity 0.88)
- Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation (NeurIPS 2026 · similarity 0.87)
- Combinatorial Stochastic-Greedy Bandit (AAAI 2024 · similarity 0.87)
- Thompson Sampling for Multi-Objective Linear Contextual Bandit (NeurIPS 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
