Refined Mechanism Design for Approximately Structured Priors via Active Regression
Authors: Christos Boutsikas, Petros Drineas, Marios Mertzanidis, Alexandros Psomas, Paritosh Verma
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design an active learning component, responsible for interacting with the bidders and outputting low-dimensional approximations of their types, and a mechanism design component, responsible for robustifying mechanisms for the low-dimensional model to work for the approximate types of the former component. On the active learning front, we cast our problem in the framework of Randomized Linear Algebra (RLA) for regression problems, allowing us to import several breakthrough results from that line of research, and adapt them to our setting. On the mechanism design front, we remove many restrictive assumptions of prior work on the type of access needed to the underlying distributions and the associated mechanisms. |
| Researcher Affiliation | Academia | Christos Boutsikas Purdue University cboutsik@purdue.edu Petros Drineas Purdue University pdrineas@purdue.edu Marios Mertzanidis Purdue University mmertzan@purdue.edu Alexandros Psomas Purdue University apsomas@cs.purdue.edu Paritosh Verma Purdue University verma136@purdue.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code or providing links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any datasets, public or otherwise. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (train, validation, test) for experimental reproduction. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details such as hyperparameters or training configurations. |