Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Multiclass U-Calibration Error and Beyond
Authors: Haipeng Luo, Spandan Senapati, Vatsal Sharan
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,491 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,100 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Spandan Senapati University of Southern California EMAIL Vatsal Sharan University of Southern California EMAIL |
| 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. |