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..
A Sample Complexity Measure with Applications to Learning Optimal Auctions
Authors: Vasilis Syrgkanis
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a new sample complexity measure, which we refer to as split-sample growth rate. For any hypothesis H and for any sample S of size m, the split-sample growth rate ˆτH(m) counts how many different hypotheses can empirical risk minimization output on any sub-sample of S of size m/2. We show that the expected generalization error is upper bounded by O q log(ˆτH(2m)). This work solely focuses on sample complexity and not computational efficiency and thus is more related to [4, 9, 10, 2]. The latter work, uses tools from supervised learning, such as pseudodimension [12] (a variant of VC dimension for real-valued functions), compression bounds [8] and Rademacher complexity [12, 14] to bound the sample complexity of simple auction classes. Our work introduces a new measure of sample complexity, which is a strengthening the Rademacher complexity analysis and hence could also be of independent interest outside the scope of the sample complexity of optimal auctions. |
| Researcher Affiliation | Industry | Vasilis Syrgkanis Microsoft Research EMAIL |
| 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 about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets, thus no information about public datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, thus no information about training/validation/test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |