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..
Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting
Authors: Zhao Haoyu, Chen Wei6893-6900
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To our knowledge, this paper is the ο¬rst to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound O(min{ ST, V 1 3 T 2 3 }), where S is the number of switches in the distribution, and V is the sum of total variation, and S and V are not needed to be known by the algorithm. We also prove regret lower bounds Ξ©( ST) in the switching case and Ξ©( V 1 3 T 2 3 ) in the dynamic case, showing that our algorithm has nearly optimal non-stationary regret. |
| Researcher Affiliation | Collaboration | Haoyu Zhao,1 Wei Chen2 1IIIS, Tsinghua University, Beijing, China 2Microsoft Research, Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Elim-NS |
| Open Source Code | No | The paper mentions a technical report on arXiv (Zhao and Chen 2019a) but does not provide any links to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and regret analysis; it does not perform empirical experiments with datasets, thus no training dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and regret analysis; it does not perform empirical experiments with datasets, thus no validation dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments or their implementation, therefore no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, so it does not provide details on experimental setup such as hyperparameters or training settings. |