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..
Projection-Free Online Convex Optimization via Efficient Newton Iterations
Authors: Khashayar Gatmiry, Zak Mhammedi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents new projection-free algorithms for Online Convex Optimization (OCO) over a convex domain K Rd... As our main contribution, we show how the stability of the Newton iterates can be leveraged to only compute the inverse Hessian a vanishing fractions of the rounds, leading to a new efficient projection-free OCO algorithm with a state-of-the-art regret bound. |
| Researcher Affiliation | Academia | Khashayar Gatmiry MIT EMAIL Zakaria Mhammedi MIT EMAIL |
| Pseudocode | Yes | Algorithm 1 BARONS: Barrier-Regularized Online Newton Step |
| Open Source Code | No | No statements or links regarding open-source code for the described methodology are provided in the paper. |
| Open Datasets | No | The paper is a theoretical work focusing on algorithm design and regret analysis, and does not conduct empirical studies or use datasets for training. |
| Dataset Splits | No | The paper is a theoretical work and does not perform experiments, thus it does not describe any dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is a theoretical work and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper focuses on theoretical analysis and algorithm design, and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameter values or training configurations. |