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].
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. |