Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting
Authors: Jonathan Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper focuses on the theoretical complexity of the problem; the relationship of the techniques in this work to efficient matrix completion in practice is an interesting direction for future research. (from Section 1 Introduction). Also, the NeurIPS checklist explicitly states: This is a theory paper where we give a new algorithm for semi-random matrix completion and prove that it achieves improved guarantees in terms of runtime, accuracy and noise tolerance. |
| Researcher Affiliation | Collaboration | MIT, kelner@mit.edu. Microsoft Research, jerrl@microsoft.com. MIT, cliu568@mit.edu. Stanford University, sidford@stanford.edu. University of Texas at Austin, kjtian@cs.utexas.edu. |
| Pseudocode | Yes | Algorithm 1: Sparsify(Osr [0,1](D), U, τ, γ, p, δ) |
| Open Source Code | No | The paper does not include experiments. The paper does not provide concrete access to source code. It is a theoretical paper presenting algorithms and proofs without experimental implementation details or code release. |
| Open Datasets | No | The paper does not include experiments. The paper focuses on theoretical analysis and algorithm design and does not describe any training or experimental datasets. |
| Dataset Splits | No | The paper does not include experiments. The paper is theoretical and does not present experimental results, therefore it does not specify training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not include experiments. The paper does not provide specific hardware details as it is a theoretical work and does not report on experimental runs requiring particular computational resources. |
| Software Dependencies | No | The paper does not include experiments. The paper focuses on theoretical algorithms and proofs, and as such, does not specify software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper does not include experiments. The paper is theoretical and does not present experimental results, therefore it does not provide specific experimental setup details or hyperparameters. |