Logarithmic Regret in Feature-based Dynamic Pricing
Authors: Jianyu Xu, Yu-Xiang Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical experiments to validate EMLP and ONSP. In comparison with the existing work, we implement a discretized EXP-4 [Auer et al., 2002] algorithm for pricing, as is introduced in Cohen et al. [2020] (in a slightly different setting). We will test these three algorithms in both stochastic and adversarial settings. |
| Researcher Affiliation | Academia | Jianyu Xu Department of Computer Science University of California, Santa Barbara Santa Barbara, CA 93106 xu_jy15@ucsb.edu Yu-Xiang Wang Department of Computer Science University of California, Santa Barbara Santa Barbara, CA 93106 yuxiangw@cs.ucsb.edu |
| Pseudocode | Yes | Algorithm 1 Epoch-based max-likelihood pricing (EMLP) and Algorithm 2 Online Newton Step Pricing (ONSP) |
| Open Source Code | Yes | We included all codes and data in the supplementary material, along with a Readme document as instructions of running the program and reproduce our results. |
| Open Datasets | No | In the numerical experiments, we only used simulated data that has nothing to do with the natural sciences and do not include human subjects." The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions running experiments for a certain number of rounds (T = 2^16) and repeating them multiple times, but it does not specify any training, validation, or test dataset splits in terms of percentages, counts, or predefined partitioning strategies. |
| Hardware Specification | No | We just ran all numerical experiments on a laptop. We did mention that the experiment of EXP-4 is very time-consuming." This statement only mentions a 'laptop' which is a general computing device, without providing specific details like CPU models, GPU models, or memory amounts. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9), needed to replicate the experiment. |
| Experiment Setup | Yes | Basically, we assume d = 2, B1 = B2 = B = 1 and Nt N(0, σ2) with σ = 0.25. In both settings, we conduct EMLP and ONSP for T = 2^16 rounds. For ONSP, we empirically select γ and ϵ that accelerates the convergence, instead of using the values specified in Lemma 11. |