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 [1].
Subspace Learning with Partial Information
Authors: Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose several efficient algorithms for this task, and analyze their sample complexity. We also provide several lower bounds on the sample complexity that can be attained by any algorithm. In this paper we propose efficient algorithms for both settings and analyze their sample complexity. We also provide several lower bounds on the sample complexity that can be attained by any algorithm. (Abstract and Introduction) The paper includes sections such as "2.2 Analysis of POPCA", "2.3 Optimality of POPCA", "3.1.3 Analysis of MBEG", and "Appendix A. Proof of Theorem 5" which describe theoretical analyses and proofs. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering The Hebrew University Jerusalem, Israel; Department of Electrical Engineering Technion, Israel Institute of Technology Haifa, Israel. The email addresses also show academic domains (e.g., .huji.ac.il, .ee.technion.ac.il). |
| Pseudocode | Yes | The paper contains Algorithm 1 POPCA, Algorithm 2 Matrix Bandit Exponentiated Gradient, and Algorithm 3 Decomposition Procedure. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit code release statement) for source code. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and analysis. It does not mention using any specific datasets for experiments or provide concrete access information for publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset usage, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental results that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental implementations requiring specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup, hyperparameters, or system-level training settings. |