Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems
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Updated
Oct 7, 2024 - MATLAB
Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems
Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multiobjective Problems
A Dual Surrogate-based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems
High-Dimensional Expensive Optimization by Classification-based Multiobjective Evolutionary Algorithm with Dimensionality Reduction
Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net
Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization
Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series
A high-performance distributed deep learning system targeting large-scale and automated distributed training.
Random Forest Two Sample Testing
R Package: Adaptively weighted group lasso for semiparametic quantile regression models
Simple and efficient Python package for modeling d-dimensional Bravais lattices in solid state physics.
Octree/Quadtree/N-dimensional linear tree
Implements "Clustering a Million Faces by Identity"
[TMLR' 24] High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
This is a repository associated with the chapter book "Towards optimal sampling for learning sparse approximations in high dimensions" by Ben Adcock, Juan M. Cardenas, Nick Dexter and Sebastian Moraga to be published by Springer in late 2021, available at https://arxiv.org/abs/2202.02360
This is a repository associated with the paper "Near-optimal sampling strategies for multivariate function on general domains" by Ben Adcock and Juan M. Cardenas available at https://epubs.siam.org/doi/10.1137/19M1279459 and https://arxiv.org/abs/1908.01249
Optimal Transport - Monge, Bregman and Occam Estimator (A Short Tutorial)
PH-tree (Permutation Hierarchical Tree) implementation in Go.
Bayesian optimization with Standard Gaussian Processes on high dimensional benchmarks
A Flexible and Powerful Parameter Server for large-scale machine learning
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