Local Minimax Complexity of Stochastic Convex Optimization
Authors: sabyasachi chatterjee, John C. Duchi, John Lafferty, Yuancheng Zhu
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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. |