bayesian optimization
A probabilistic model-based optimization approach, particularly suited for optimizing expensive-to-evaluate functions. It systematically explores the parameter space using a surrogate model to efficiently find the optimal parameters with as few evaluations as possible.
- Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
- Amortized Variational Transdimensional Inference
- BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem
- BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
- Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble
- Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
- Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
- DynaNav: Dynamic Feature and Layer Selection for Efficient Visual Navigation
- Exploring and Exploiting Model Uncertainty in Bayesian Optimization
- FSEO: Few-Shot Evolutionary Optimization via Meta-Learning for Expensive Multi-Objective Optimization
- Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
- Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
- Informed Initialization for Bayesian Optimization and Active Learning
- Learning to Generalize: An Information Perspective on Neural Processes
- MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions
- Martingale Posterior Neural Networks for Fast Sequential Decision Making
- No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
- Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
- Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
- Robust and Computation-Aware Gaussian Processes
- Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
- Steering Generative Models with Experimental Data for Protein Fitness Optimization
- Thompson Sampling in Function Spaces via Neural Operators
- Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project