Combining Bayesian optimization and Lipschitz optimization
From MaRDI portal
Publication:2303661
DOI10.1007/s10994-019-05833-yzbMath1446.90129arXiv1810.04336OpenAlexW2969230934WikidataQ127371244 ScholiaQ127371244MaRDI QIDQ2303661
Sharan Vaswani, Mohamed Osama Ahmed, Mark Schmidt
Publication date: 4 March 2020
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.04336
Related Items (5)
Comparative analysis of machine learning methods for active flow control ⋮ Integrating \(\varepsilon \)-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems ⋮ Tracking and Regret Bounds for Online Zeroth-Order Euclidean and Riemannian Optimization ⋮ (Global) optimization: historical notes and recent developments ⋮ Novel local tuning techniques for speeding up one-dimensional algorithms in expensive global optimization using Lipschitz derivatives
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
- A literature survey of benchmark functions for global optimisation problems
- An experimental methodology for response surface optimization methods
- An informational approach to the global optimization of expensive-to-evaluate functions
- Computational intelligence in expensive optimization problems
- Efficient global optimization of expensive black-box functions
- Lipschitzian optimization without the Lipschitz constant
- Global optimization in action. Continuous and Lipschitz optimization: algorithms, implementations and applications
- Derivative-free optimization: a review of algorithms and comparison of software implementations
- Introduction to nonlinear and global optimization
- An algorithm for finding the absolute extremum of a function
- Parallel Bayesian Global Optimization of Expensive Functions
- Convergence rates of efficient global optimization algorithms
- A Sequential Method Seeking the Global Maximum of a Function
This page was built for publication: Combining Bayesian optimization and Lipschitz optimization
