Sample Complexity of Automated Mechanism Design
Authors: Maria-Florina F. Balcan, Tuomas Sandholm, Ellen Vitercik
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we provide the first sample complexity analysis for the standard hierarchy of deterministic combinatorial auction classes used in automated mechanism design. In particular, we provide tight sample complexity bounds on the number of samples needed to guarantee that the empirical revenue of the designed mechanism on the samples is close to its expected revenue on the underlying, unknown distribution over bidder valuations, for each of the auction classes in the hierarchy. In addition to helping set automated mechanism design on firm foundations, our results also push the boundaries of learning theory. |
| Researcher Affiliation | Academia | Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 {ninamf,sandholm,vitercik}@cs.cmu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | No | This is a theoretical paper and does not involve training models on datasets. Therefore, no information about publicly available or open datasets for training is provided. |
| Dataset Splits | No | This is a theoretical paper focused on sample complexity bounds and does not describe empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings. |