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..
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
Authors: Yining Wang, Sivaraman Balakrishnan, Aarti Singh
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a local minimax framework to study the fundamental difficulty of optimizing smooth functions with adaptive function evaluations. Our main results are to characterize the local convergence rates Rnpf0q for a wide range of reference functions f0 P F. We prove local minimax lower bounds that match the n α{p2α d αβq upper bound, up to logarithmic factors in n. |
| Researcher Affiliation | Academia | Yining Wang, Sivaraman Balakrishnan, Aarti Singh Department of Machine Learning and Statistics Carnegie Mellon University, Pittsburgh, PA, 15213, USA EMAIL, EMAIL |
| Pseudocode | No | The paper includes a conceptual illustration of an algorithm in Figure 1 and describes its procedure in Section A. However, it does not present a formal, structured pseudocode block labeled "Algorithm" or "Pseudocode". |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and models a system (Eq. 1) but does not use real-world datasets for training or experimentation. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Therefore, no information regarding training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software components or their versions required for reproduction. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical derivations and bounds. It does not describe any empirical experimental setup, hyperparameters, or training configurations. |