Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Authors: Alexey Drutsa
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a novel non-parametric learning algorithm that is horizon-independent and has tight strategic regret upper bound of Θ(T d/(d+1)). We also non-trivially generalize several value-localization techniques of noncontextual repeated auctions to make them effective in the considered contextual non-parametric learning of the buyer valuation function. ... Theorem 1. Let d 1, γ0 (0, 1), and A be the PELS algorithm with parameters set as in Eq.(3). Then, for any discount γ (0, γ0], distribution D, and feature vectors x1:T XT , the regret of PELS against the strategic buyer with a Lipschitz valuation v Lip L(X) is upper bounded: SRegret(T,A,v,γ,x1:T ,D) C(N0(T +N0)d) 1 d+1 , |
| Researcher Affiliation | Collaboration | 1Yandex, Moscow, Russia 2Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia. |
| Pseudocode | Yes | The pseudo-code of PELS is presented in Alg. B.1 in Appendix B of Supplementary Materials. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is being released or provide a link to a repository. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments involving datasets. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments or specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |