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..
SDCA without Duality, Regularization, and Individual Convexity
Authors: Shai Shalev-Shwartz
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove linear convergence rates even if individual loss functions are non-convex, as long as the expected loss is strongly convex. |
| Researcher Affiliation | Academia | Shai Shalev-Shwartz EMAIL School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel |
| Pseudocode | Yes | Algorithm 1: Dual-Free SDCA for Regularized Objectives |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis of algorithms and does not describe experiments with specific datasets, thus no access information for training data is provided. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe experiments requiring dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware for execution. |
| Software Dependencies | No | The paper is theoretical and does not discuss software implementations or their specific version dependencies. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their analysis, but it does not detail an experimental setup or hyperparameters for empirical evaluation. |