Information-theoretic Limits of Online Classification with Noisy Labels
Authors: Changlong Wu, Ananth Grama, Wojciech Szpankowski
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contributions in this paper establish the fundamental limits of minimax risk in (3) by providing nearly matching lower and upper bounds across a wide range of hypothesis classes H and noisy kernels K. This is a pure theory paper. |
| Researcher Affiliation | Academia | Changlong Wu Ananth Grama Wojciech Szpankowski CSo I, Purdue University wuchangl@hawaii.edu, {ayg,szpan}@purdue.edu |
| Pseudocode | Yes | Algorithm 1: Predictor via Pairwise Hypothesis Testing and Algorithm 2: Exponential Weighted Average (EWA) estimator |
| Open Source Code | No | This is a pure theory paper. No statement about releasing open-source code for the methodology described in the paper is found. |
| Open Datasets | No | This is a pure theory paper and does not involve the use of a dataset for training or evaluation. |
| Dataset Splits | No | This is a pure theory paper and does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | This is a pure theory paper and does not specify any hardware used for running experiments. |
| Software Dependencies | No | This is a pure theory paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a pure theory paper and does not include details about an experimental setup, such as hyperparameters or training configurations. |