Learning to Price Against a Moving Target
Authors: Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here we study the problem where the buyer s value is a moving target, i.e., they change over time either by a stochastic process or adversarially with bounded variation. In either case, we provide matching upper and lower bounds on the optimal revenue loss. Since the target is moving, any information learned soon becomes out-dated, which forces the algorithms to keep switching between exploring and exploiting phases. |
| Researcher Affiliation | Collaboration | 1Google Research, New York, NY, USA 2Department of Computer Sciences, University of Wisconsin Madison, Madison, WI, USA. |
| Pseudocode | Yes | Algorithm 1 Symmetric-loss minimizing algorithm for adversarial buyer with known changing rate ϵ |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation, thus no public dataset access information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with datasets, so no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report experimental results; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report experimental results; therefore, no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical bounds, so no specific experimental setup details or hyperparameters are provided. |