Author

C. Cartis

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Preprint Aug 2026

A note on the complexity of random subspace model-based methods for derivative-free optimization

We demonstrate that, with a suitable rescaling, using Johnson-Lindenstrauss transforms (JLTs) in the random subspace model-based derivative-free optimization (DFO) algorithm from [Cartis&Roberts, Math. Prog. 199 (2023)] achieves an improved worst-case evaluation complexity bound compared to'unscaled'JLTs. This improved...

C. Cartis, Lindon Roberts · 2 citations

#machine learning Oct 2025

Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization

This analysis clarifies metric shaping and retraining choices critical for scalable latent-space BO, combining SDR with VAE-based LSBO and demonstrates the effectiveness of VAE-based BO for nonlinear low-dimensional structures.

Luo Long, C. Cartis, Paz Fink Shustin · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Stochastic Nonconvex Bilevel Optimization: Improved Rates Without Rare-Visit Assumption

This work shows that a simple denominator-only regularization of the DBGD multiplier eliminates the need for such an assumption while preserving fast convergence rates, and improves upon the best assumption-free complexities.

Daniel Cortild, Mathias Staudigl, J. Peypouquet et al. · 0 citations