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Found 19 papers, 14 papers with code
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods.
Ecologically mapped neuronal identity: Towards standardizing activity across heterogeneous experiments
We review the possibility of adding area-specific environmental enrichment and automatized behavioral tasks to identify neurons in specific brain areas.
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
We use Bayesian Optimization (BO) and propose a novel surrogate modeling approach for efficiently handling a large number of binary and categorical parameters.
Discovering Many Diverse Solutions with Bayesian Optimization
Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions.
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
We prove that under suitable reparameterizations, the BO policy that maximizes the probabilistic objective is the same as that which maximizes the AF, and therefore, PR enjoys the same regret bounds as the original BO policy using the underlying AF.
Sparse Bayesian Optimization
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions.
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives.
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy.
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-box functions.
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
It was based on tuning (validation set) performance of standard machine learning models on real datasets.
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box objective functions.
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learning, e. g., when sampling from high-dimensional Gaussians $\mathcal{N}(\mathbf 0, \mathbf K)$ or whitening a vector $\mathbf b$ against covariance matrix $\mathbf K$.
Efficient Rollout Strategies for Bayesian Optimization
Non-myopic acquisition functions consider the impact of the next $h$ function evaluations and are typically computed through rollout, in which $h$ steps of BO are simulated.
Scalable Constrained Bayesian Optimization
The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering.
Scalable Global Optimization via Local Bayesian Optimization
This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems.
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT).
Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.
Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.
2HDMC - Two-Higgs-Doublet Model Calculator
This manual describes the public code 2HDMC which can be used to perform calculations in a general, CP-conserving, two-Higgs-doublet model (2HDM).
High Energy Physics - Phenomenology
