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..
A Nearly-Optimal Bound for Fast Regression with $\ell_∞$ Guarantee
Authors: Zhao Song, Mingquan Ye, Junze Yin, Lichen Zhang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | On the algorithmic side, we prove that there exists a distribution of dense sketching matrices with m = ϵ 2d log3(n/δ) such that solving the sketched regression problem gives the ℓ guarantee, with probability at least 1 δ. Moreover, we develop a novel analytical framework for ℓ guarantee regression that utilizes the Oblivious Coordinate-wise Embedding (OCE) property introduced in (Song & Yu, 2021). Our analysis is much simpler and more general than that of (Price et al., 2017). |
| Researcher Affiliation | Collaboration | 1Adobe Research 2University of Illinois at Chicago 3Boston University 4MIT. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not discuss the use of any specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |