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..
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. |