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].
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. |