Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..

Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes

Authors: Weijia Shao

ICML 2024 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical This work proposes an algorithm improving the dimensionality dependence for gradient-free optimisation over cross-polytopes... The idea can be further applied to optimising nonsmooth and non-convex functions. We propose an algorithm with a convergence depending on O(d), which is the best-known dimensionality dependence... Despite the theoretically advantageous characteristics of the proposed algorithms, it is imperative to acknowledge the practical limitations of the idea... In any case, a systematic examination of the proposed algorithm s performance through rigorous experimentation is required.
Researcher Affiliation Academia 1Unit 2.6 Workplaces, Safety of Machinery, Operational Safety, Federal Institute for Occupational Safety and Health, Dresden, Germany.
Pseudocode Yes Algorithm 1 Mirror Descent Framework Algorithm 2 Gradient Estimator: GE(f, x, λ, γ)
Open Source Code No The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described.
Open Datasets No This is a theoretical paper focusing on algorithm design and complexity analysis; it does not include empirical studies with datasets.
Dataset Splits No This is a theoretical paper focusing on algorithm design and complexity analysis; it does not include empirical studies with dataset splits for validation.
Hardware Specification No This is a theoretical paper that does not report on conducted experiments, therefore no hardware specifications are mentioned.
Software Dependencies No This is a theoretical paper that does not report on conducted experiments, therefore no software dependencies with version numbers are mentioned.
Experiment Setup No This is a theoretical paper that does not report on conducted experiments, therefore no experimental setup details like hyperparameters or training configurations are provided.