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].
Non-stochastic Budgeted Online Pricing with Semi-Bandit Feedback
Authors: Xiang Liu, Hau Chan, Minming Li, Weiwei Wu, Long Tran-Thanh
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct simulation experiments to show that the proposed policy outperforms the baseline algorithms. We empirically evaluate the proposed main policy GAP by comparing it with four state-of-the-art baseline algorithms from the literature. |
| Researcher Affiliation | Academia | 1Southeast University 2The Chinese University of Hong Kong 3University of Nebraska-Lincoln 4City University of Hong Kong 5The University of Warwick EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: The Granularity-based Pricing (GAP) Policy |
| Open Source Code | No | No explicit statement or link to open-source code for the methodology described in this paper is provided. |
| Open Datasets | No | We follow the standard setup for evaluating nonstochastic bandits (Zimmert, Luo, and Wei 2019; Alipour Fanid, Dabaghchian, and Zeng 2021). In particular, in our experiments the degree of the non-stochasticity can be quantified by the stochastically constrained adversaries. That is, the mean cost/value of each seller switches while staying unchanged for phases that are increasing exponentially in length (which is a common metric in non-stochastic bandit literature). The cost of seller si is uniformly set as follows, ci t [0.1, 0.2] if t belongs to an odd phase, [0.2, 1] otherwise and the value of each seller similarly switches between range [0.1, 0.2] and [0.2, 1]. |
| Dataset Splits | No | The paper uses a simulated environment for experiments, where seller costs and values are defined programmatically. Therefore, traditional training/test/validation dataset splits are not applicable or mentioned. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | By setting ϵ = c3/2 min/ B, η = ϵ/vmax and γ = ϵ/cmin, the expected α-regret of GAP is at most O n vmax B/cmin ln B where α = vmin (2 cmin)vmax . To evaluate the impact of the budget, we vary the budget in the range [5000, 40000] with the increment of 5000 and let B = 20000 by default. We also let the number of sellers, i.e., n, be selected from {5, 15, 25, 35, 45}, and let n = 25 by default. |