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..
Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems
Authors: Koulik Khamaru, Martin J. Wainwright
JMLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our methods and theory via applications to the problems of best subset selection, robust estimation, mixture density estimation, and shape-from-shading reconstruction. ... Some reconstruction experiments: In order to illustrate the behavior of our method for this problem, we considered two synthetic images for simulated experiments. ...Figure 2 shows the performances of the prox-type method and CCCP for synthetic data simulated as above, with problem parameters (n, p) = (190, 300) and (n, p) = (380, 600) and different choices of sparsity s. |
| Researcher Affiliation | Collaboration | Koulik Khamaru EMAIL Martin J. Wainwright , , EMAIL Department of Statistics Department of Electrical Engineering & Computer Sciences University of California Berkeley Berkeley, CA 94720-1776, USA Voleon Group Berkeley, CA |
| Pseudocode | Yes | Algorithm 1 Subgradient-type method Algorithm 2 Proximal-type algorithm Algorithm 3 Frank-Wolfe type method Algorithm 4 Gradient descent with backtracking |
| Open Source Code | No | The paper does not provide any explicit statements about releasing code or links to source code repositories. |
| Open Datasets | Yes | The first one is a 256 256 image of Mozart (Zhang et al., 1999), and the second one is a 128 128 image of Vase. |
| Dataset Splits | No | The paper describes the generation of synthetic data but does not specify how this data was split into training, validation, or test sets for experiments. |
| Hardware Specification | No | The paper mentions runtimes for experiments ("The runtime for Mozart-example was 87 seconds, whereas the runtime for Vase-example was 39 seconds") but does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used. |
| Software Dependencies | No | The paper describes various algorithms and their theoretical properties. While it discusses implementation aspects, it does not specify any software libraries, frameworks, or their version numbers used for the experiments. |
| Experiment Setup | Yes | For both the algorithms, the tolerance level η was set to η = 10 8, whereas the maximum number of iterations was 1000. In all our simulations we used same initializations for both the algorithms. |