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 with Time-Varying Constraints
Authors: Dan Garber, Ben Kretzu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an algorithm that, on a sequence of length T and using overall T calls to the LOO, guarantees O(T 3/4) regret w.r.t. the losses and O(T 7/8) constraints violation (ignoring all quantities except for T). We present a more efficient algorithm that does not require the latter optimization oracle but only first-order access to the time-varying constraints, and achieves similar bounds w.r.t. the entire sequence. Importantly, our regret bounds w.r.t. the loss functions f1, . . . , f T , both in the full-information and bandit settings, match up to logarithmic terms the current state-of-the-art bounds (in terms of the horizon T and dimension n) for projection-free OCO (i.e., without additional time-varying constraints)... |
| Researcher Affiliation | Academia | 1Technion Israel Institute of Technology, Haifa, Israel. Correspondence to: Ben Kretzu <EMAIL>, Dan Garber <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Approximately-Feasible Projection Oracle via a Linear Optimization Oracle (see (Garber & Kretzu, 2023)) and Algorithm 2 Separating Hyperplane via Frank-Wolfe and Algorithm 3 LOO-based Drift-plus-Penalty Method and Algorithm 4 LOO-based Primal-Dual Method and Algorithm 5 LOO-based Bandit Primal-Dual Method. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific public datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, thus no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with concrete hyperparameter values or training configurations. |