Optimal Multiclass U-Calibration Error and Beyond
Authors: Haipeng Luo, Spandan Senapati, Vatsal Sharan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We resolve this question by showing that the optimal U-calibration error is Θ(KT) we start with a simple observation that the Follow-the-Perturbed Leader algorithm of Daskalakis and Syrgkanis (2016) achieves this upper bound, followed by a matching lower bound constructed with a specific proper loss |
| Researcher Affiliation | Academia | Haipeng Luo University of Southern California haipengl@usc.edu Spandan Senapati University of Southern California ssenapat@usc.edu Vatsal Sharan University of Southern California vsharan@usc.edu |
| Pseudocode | Yes | Algorithm 1 FTPL with geometric noise for U-calibration |
| Open Source Code | No | The paper does not include an unambiguous statement of code release or a link to a repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report hardware specifications for experiments. |
| Software Dependencies | No | The paper is theoretical and does not report software dependencies with version numbers for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup. |