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-Stationary Contextual Pricing with Safety Constraints
Authors: Dheeraj Baby, Jianyu Xu, Yu-Xiang Wang
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an algorithm Pro DR (Algorithm 1) that attains an optimal O(d3(T 1 3 C 2 3 T 1)) dynamic regret (modulo dependencies in d and log T) for the setting of proper OCO with co-variates under exp-concave losses (see Section 3.1). We construct an algorithm PDRP (Algorithm 2) with a base learner Pro DR, which solves the non-stationary contextual pricing problem with strictly log-concave noise. We define the dynamic regret of contextual pricing as Eq.(3) and show that PDRP achieves a O(d3(T 1 3 C 2 3 T 1)) dynamic regret guarantee (see Section 3.2). We show that any algorithm must incur a dynamic regret of Ω(T 1 3 C 2 3 T 1) in the contextual pricing problem, which says that PDRP is minimax optimal up to d and log T factors (see Section 3.3). |
| Researcher Affiliation | Academia | Dheeraj Baby EMAIL Department of Computer Science University of California, Santa Barbara; Jianyu Xu EMAIL Department of Computer Science University of California, Santa Barbara; Yu-Xiang Wang EMAIL Department of Computer Science University of California, Santa Barbara |
| Pseudocode | Yes | Algorithm 1 Proper Dynamic Regret minimization (Pro DR); Algorithm 2 Proper Dynamic Regret Pricing (PDRP) |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper describes a linear noisy valuation model for customers and market noises drawn from a known strictly log-concave distribution. It does not mention the use of any specific publicly available datasets nor provides access information for any data used. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, therefore no dataset splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementation details that would list software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and regret analysis rather than empirical evaluation, so it does not include details on experimental setup or hyperparameters. |