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..
Riemannian Projection-free Online Learning
Authors: Zihao Hu, Guanghui Wang, Jacob D. Abernethy
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present methods for obtaining sub-linear regret guarantees in online geodesically convex optimization on curved spaces for two scenarios: when we have access to (a) a separation oracle or (b) a linear optimization oracle. For geodesically convex losses, and when a separation oracle is available, our algorithms achieve O(T 1/2), O(T 3/4) and O(T 1/2) adaptive regret guarantees in the full information setting, the bandit setting with one-point feedback and the bandit setting with two-point feedback, respectively. When a linear optimization oracle is available, we obtain regret rates of O(T 3/4) for geodesically convex losses and O(T 2/3 log T) for strongly geodesically convex losses. |
| Researcher Affiliation | Collaboration | Zihao Hu , Guanghui Wang , Jacob Abernethy , College of Computing, Georgia Institute of Technology Google Research |
| Pseudocode | Yes | Algorithm 1: Infeasible Riemannian OGD, Algorithm 2: Infeasible Projection onto (1 δ)K with a Riemannian Separation Oracle, Algorithm 3: Infeasible R-OGD with a separation oracle, Algorithm 4: One-point bandit convex optimization on manifolds with a separation oracle, Algorithm 5: Two-point bandit convex optimization on manifolds with a separation oracle, Algorithm 6: Separating Hyperplane via RFW, Algorithm 7: Closer Infeasible Projection via LOO, Algorithm 8: Block OGD on manifolds with a linear optimization oracle, Algorithm 9: Riemannian Frank-Wolfe with line-search |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the code will be released. |
| Open Datasets | No | The paper is theoretical and does not describe any experimental training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not provide an experimental setup section with hyperparameters or training configurations. |