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..
Dueling Convex Optimization with General Preferences
Authors: Aadirupa Saha, Tomer Koren, Yishay Mansour
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is an efficient algorithm with convergence rate of O(ϵ 4p) for smooth convex functions, and an optimal rate of e O(ϵ 2p) when the objective is both smooth and strongly convex, where p is the minimal degree (with a non-zero coefficient) in the transfer s series expansion about the origin. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Illinois, Chicago, US 2Blavatnik School of Computer Science, Tel Aviv University, Israel 3Google Research, Tel Aviv, Israel. Correspondence to: Aadirupa Saha <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Projected Dueling Descent (PDD) ... Algorithm 2 Epoch-PDD |
| Open Source Code | No | The paper does not provide any statements or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical, focusing on algorithm design and convergence analysis for convex optimization. It does not conduct empirical studies that would involve datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets or experiments, therefore, there are no dataset splits mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and convergence proofs. It does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementation. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and presents algorithms with convergence proofs. It does not describe any empirical experiments, and therefore, no experimental setup details, such as hyperparameters or system-level training settings, are provided. |