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..
Sample Complexity of Uniform Convergence for Multicalibration
Authors: Eliran Shabat, Lee Cohen, Yishay Mansour
NeurIPS 2020 | Venue PDF | 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 EMAIL Lee Cohen Tel Aviv University EMAIL Yishay Mansour Tel Aviv University and Google Research EMAIL |
| 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. |