Two-stage local method for query-efficient high-dimensional Bayesian optimization
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Aug 27, 2026 - Python
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high-dimensional-optimization
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[ICASSP'26] scalable, gradient-free optimizer for high-dimensional problems
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May 7, 2026 - Python
Research framework for geometric reformulation of black-box optimization. SGOLab proposes a domain-codomain decoupling principle, navigating the search space through an independent geometric reference system rather than explicit landscape reconstruction.
python research optimization black-box-optimization mathematical-optimization derivative-free-optimization high-dimensional-optimization theoretical-framework geometric-optimization
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Jun 12, 2026
A memory-efficient, gradient-free zeroth-order (derivative-free) optimizer designed to solve the "Curse of Dimensionality" in Black-Box optimization and memory-constrained Machine Learning. It provides an O(log D) gradient estimation approach that can successfully train Neural Networks without ever calculating analytical derivatives or Backprop
python machine-learning deep-learning black-box-optimization derivative-free-optimization gradient-free-optimization zeroth-order-optimization neural-network-training high-dimensional-optimization backpropagation-alternative denoised-gradient-estimation
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Aug 7, 2026 - Python
High-dimensional sparse regression in R: penalized Huber, SVM, quantile, and Wilcoxon rank regression via the finite smoothing algorithm. Fast C++ (Rcpp) coordinate-descent kernels with elastic-net penalties, cross-validation, and SCAD/MCP non-convex penalties.
r rcpp cpp machinelearning support-vector-machines quantile-regression huber-loss-regression high-dimensional-optimization elastic-net-regularization statstistics rank-regression
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Sep 18, 2026 - R
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