Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Within an Instance for Designing High-Revenue Combinatorial Auctions
Authors: Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
IJCAI 2021 | Venue PDF | 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. EMAIL |
| 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. |