Learning Functional Distributions with Private Labels
Authors: Changlong Wu, Yifan Wang, Ananth Grama, Wojciech Szpankowski
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the minimax expected KL-risk is of order Θ( pT log |F|) for finite hypothesis class F and any non-trivial noise level. We then extend this result to general infinite classes via the concept of stochastic sequential covering and provide matching lower and upper bounds for a wide range of natural classes. |
| Researcher Affiliation | Academia | 1Center for Science of Information, Department of Computer Science, Purdue University. |
| Pseudocode | Yes | Algorithm 1 Noisy Smooth Bayesian Predictor |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments, thus no dataset is used or made publicly available for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no dataset splits for training, validation, or testing are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments. Therefore, no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |