black-box optimization
A class of optimization methods that seeks to find the optimum of objective functions without needing to know their internal workings or derivatives, commonly applied when dealing with complex or noisy functions.
- Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble
- Convergence Rates of Constrained Expected Improvement
- DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization
- Neural Evolution Strategy for Black-box Pareto Set Learning
- Nonlinear Laplacians: Tunable principal component analysis under directional prior information
- Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
- ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge