Learning Within an Instance for Designing High-Revenue Combinatorial Auctions
Authors: Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a new framework for designing truthful, high-revenue (combinatorial) auctions for limited supply. Our mechanism learns within an instance. It generalizes and improves over previously-studied random-sampling mechanisms. It first samples a participatory group of bidders, then samples several learning groups of bidders from the remaining pool of bidders, learns a highrevenue auction from the learning groups, and finally runs that auction on the participatory group. ... We prove guarantees on the performance of our mechanism based on a market-shrinkage term and a new complexity measure we coin partition discrepancy. |
| Researcher Affiliation | Collaboration | Maria-Florina Balcan1 , Siddharth Prasad1 and Tuomas Sandholm1,2,3,4 1School of Computer Science, Carnegie Mellon University 2Optimized Markets, Inc. 3Strategic Machine, Inc. 4Strategy Robot, Inc. {ninamf, sprasad2, sandholm}@cs.cmu.edu |
| Pseudocode | Yes | Learning-within-an-instance mechanism (LWI) Parameters: p, q, N 1. Draw a group of participatory buyers Spar p S. 2. Draw learning groups of buyers S1, . . . , SN q S \ Spar. 3. Find the mechanism c M M that maximizes empirical revenue 1 N PN t=1 Rev M(St) over the learning groups. 4. Apply mechanism c M to Spar. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or provide information about any publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |