Dynamic Pricing in High-dimensions
Authors: Adel Javanmard, Hamid Nazerzadeh
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in T. More specifically, the regret of our algorithm is of O(s0 log d log T). Furthermore, we show that no policy can obtain regret better than O(s0(log d + log T)). In addition, we propose a generalization of our policy to a setting that the market noise distribution is unknown but belongs to a parametrized family of distributions. This policy obtains regret of O( p (log d)T). We further show that no policy can obtain regret better than O( T) in such environments. |
| Researcher Affiliation | Academia | Adel Javanmard EMAIL Department of Data Sciences and Operations Marshall School of Business University of Southern California Los Angeles, CA 90089 , USA Hamid Nazerzadeh EMAIL Department of Data Sciences and Operations Marshall School of Business University of Southern California Los Angeles, CA 90089 , USA |
| Pseudocode | Yes | Algorithm 1: RMLP policy for dynamic pricing; Algorithm 2: RMLP Policy for dynamic pricing under the nonlinear setting; Algorithm 3: RMLP-2 policy for dynamic pricing |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes a theoretical framework and does not mention the use of any specific publicly available or open datasets for empirical evaluation. It refers to 'feature vectors xt are sampled independently from a fixed, but a priori unknown, distribution PX', which is part of the theoretical model, not an external dataset. |
| Dataset Splits | No | The paper does not describe any experimental evaluation using datasets, therefore, there are no mentions of dataset splits (e.g., training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers used for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not contain details about an experimental setup, including hyperparameters or system-level training settings. |