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..
Local Minimax Complexity of Stochastic Convex Optimization
Authors: sabyasachi chatterjee, John C. Duchi, John Lafferty, Yuancheng Zhu
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The nature and practical implications of the results are demonstrated in simulations. ... 3.1 Simulations showing adaptation to the benchmark ... The simulation results are shown in the top 3 panels of Figure 2. |
| Researcher Affiliation | Academia | Yuancheng Zhu Wharton Statistics Department University of Pennsylvania; Sabyasachi Chatterjee Department of Statistics University of Chicago; John Duchi Department of Statistics Department of Electrical Engineering Stanford University; John Lafferty Department of Statistics Department of Computer Science University of Chicago |
| Pseudocode | Yes | Algorithm 1 Sign testing binary search |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for simulations: 'The function to optimize is fk(x) = 1/k|x − x |k for k = 3/2, 2 or 3. The minimum point x ∼ Unif(−1, 1) is selected uniformaly at random over the interval.' It does not provide access information for a public dataset. |
| Dataset Splits | No | The paper does not specify exact training/validation/test split percentages, sample counts, or reference predefined splits. The simulations involve generating data on the fly based on specified functions and noise models. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, library names, or version numbers used to replicate the experiments. |
| Experiment Setup | Yes | For the stochastic gradient descent algorithm, we perform T steps of update xt+1 = xt - eta(t)bg(xt) where eta(t) is a stepsize function, chosen as either eta(t) = 1/t or eta(t) = 1/sqrt(t). The function to optimize is fk(x) = 1/k|x - x |k for k = 3/2, 2 or 3. The minimum point x ~ Unif(-1, 1) is selected uniformaly at random over the interval. The oracle returns the derivative at the query point with additive N(0, sigma^2) noise, sigma = 0.1. |