Sample Complexity of Uniform Convergence for Multicalibration
Authors: Eliran Shabat, Lee Cohen, Yishay Mansour
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main results in this work are sample bounds that guarantee uniform convergence of a given class of predictors. We start by deriving a sample bound for the case of a finite hypothesis class, and derive a sample complexity bound which is logarithmic in the size of the hypothesis class. Later, for an infinite hypothesis class, we derive a sample bound that depends on the graph dimension of the class (which is an extension of the VC dimension for multiclass predictions). Finally, we derive a lower bound on the sample size required. |
| Researcher Affiliation | Collaboration | Eliran Shabat Tel Aviv University shabat.eliran@gmail.com Lee Cohen Tel Aviv University leecohencs@gmail.com Yishay Mansour Tel Aviv University and Google Research mansour.yishay@gmail.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It is a theoretical paper focused on sample complexity bounds. |
| Open Datasets | No | The paper is theoretical and does not report on experiments with datasets, thus no information on public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation that would require specific ancillary software details or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |