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 of Stochastic Gradient Descent for PCA
Authors: Ohad Shamir
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide (to the best of our knowledge) the ο¬rst eigengap-free convergence guarantees for SGD in the context of PCA. This also partially resolves an open problem posed in (Hardt & Price, 2014). Moreover, under an eigengap assumption, we show that the same techniques lead to new SGD convergence guarantees with better dependence on the eigengap. |
| Researcher Affiliation | Academia | Ohad Shamir EMAIL Weizmann Institute of Science, Israel |
| Pseudocode | Yes | Initialize by picking a unit norm vector w0 For t = 1, . . . , T, perform wt = (I + Ξ· At)wt 1 Return w T w T |
| Open Source Code | No | The paper does not contain any statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or mention specific datasets for training. It refers to 'data x1, x2, . . . Rd' as i.i.d. from an 'unknown underlying distribution' in a theoretical context. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with concrete hyperparameter values or system-level training settings. |