Research map / Probabilistic and scientific modeling / Bayesian optimization
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use probabilistic reparameterization for acquisition optimization instead of the alternative optimization methods | Bayesian optimization with discrete or mixed inputs · Benchmark problems | log regret | Helped |
| Use probabilistic reparameterization instead of HyBO | Bayesian optimization · Mixed Int F1 | log regret | No clear effect |
| Combine probabilistic reparameterization with a trust region | Bayesian optimization · Benchmark problems | log regret | Helped |
Closest papers by meaning
- BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability (NeurIPS 2026 · similarity 0.89)
- Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints (ICML 2024 · similarity 0.89)
- Preference-Aware Constrained Multi-Objective Bayesian Optimization (Student Abstract) (AAAI 2024 · similarity 0.88)
- Informed Initialization for Bayesian Optimization and Active Learning (NeurIPS 2025 · similarity 0.88)
- Learning Surrogates for Offline Black-Box Optimization via Gradient Matching (ICML 2024 · similarity 0.88)
- Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal (NeurIPS 2024 · similarity 0.88)
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.