Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Authors: Alexey Drutsa
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 23,866 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,058 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |