global-optimization
146 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in global-optimization
Libraries
Use these libraries to find global-optimization models and implementationsMost implemented papers
Tree Search vs Optimization Approaches for Map Generation
We compare them on three different game level generation problems: Binary, Zelda, and Sokoban.
A Literature Survey of Benchmark Functions For Global Optimization Problems
Test functions are important to validate and compare the performance of optimization algorithms.
Scalable Bayesian Optimization Using Deep Neural Networks
automl/pybnn
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Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Pre-trained Gaussian Processes for Bayesian Optimization
Contrary to a common expectation that BO is suited to optimizing black-box functions, it actually requires domain knowledge about those functions to deploy BO successfully.
Global optimization of Lipschitz functions
The goal of the paper is to design sequential strategies which lead to efficient optimization of an unknown function under the only assumption that it has a finite Lipschitz constant.
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).
Scalable Global Optimization via Local Bayesian Optimization
uber-research/TuRBO
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NeurIPS 2019
This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems.
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
Our formulation involves clients synthesizing a small set of samples that approximate local loss landscapes by simulating the gradients of real images within a local region.
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
MiuLab/SlotGated-SLU
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NAACL 2018
Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
pytorch/botorch
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NeurIPS 2020
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
