A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions
Authors: Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a framework of designing non-clairvoyant dynamic mechanisms that are robust to both the estimation errors on the buyer s distributional information and the buyer s strategic behavior. We then tailor our framework to the setting of contextual auctions to develop a non-clairvoyant mechanism that achieves no-regret against 1/3-approximation of the revenue-optimal clairvoyant dynamic mechanism. The paper focuses on theoretical contributions, including mechanism design, mathematical proofs (Theorem 3.1, Lemma 3.1-3.6), and analysis of regret bounds (O(T^5/6)), without reporting any empirical studies, datasets, or experimental results. |
| Researcher Affiliation | Collaboration | Yuan Deng Duke University Durham, NC ericdy@cs.duke.edu; Sébastien Lahaie Google Research New York City, NY slahaie@google.com; Vahab Mirrokni Google Research New York City, NY mirrokni@google.com |
| Pseudocode | Yes | Figure 1: Robust Non-clairvoyant Dynamic Contextual Auction Policy. This figure presents a structured description of the 'Learning Policy' and 'Dynamic Mechanism Policy' in a step-by-step format, which functions as an algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and describes a problem framework (e.g., 'observable feature vector ζt Rd', 'feature vectors are drawn independently from a fixed distribution D') rather than using specific, named datasets or providing access information for any dataset. Therefore, there is no mention of publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with empirical data. As such, it does not provide details on dataset splits (training, validation, test) needed for reproducibility. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or computational execution. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions used in its development. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup, including hyperparameters or specific training settings. |