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..
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Authors: Chi Jin, Sham M. Kakade, Praneeth Netrapalli
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose the first provable, efficient online algorithm for matrix completion. Our proofs introduce a general framework to show that SGD updates tend to stay away from saddle surfaces and could be of broader interests to other non-convex problems. |
| Researcher Affiliation | Collaboration | Chi Jin UC Berkeley EMAIL Sham M. Kakade University of Washington EMAIL Praneeth Netrapalli Microsoft Research India EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Algorithm for PSD Matrix Completion. Algorithm 2 Online Algorithm for Matrix Completion (Theoretical) Algorithm 3 Online Algorithm for Matrix Completion (Practical) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper does not specify a publicly available or open dataset used for training. It refers to 'Initial set of uniformly random samples Ωinit' without providing access information. |
| Dataset Splits | No | The paper is theoretical and does not describe specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe the specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper discusses theoretical parameters like learning rate (η) and number of observations (T) but does not provide concrete hyperparameter values or system-level training settings for an empirical experimental setup. |