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..
Dynamic Pricing in High-dimensions
Authors: Adel Javanmard, Hamid Nazerzadeh
JMLR 2019 | Venue PDF | 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. |