Nonparametric Density Estimation under Distribution Drift
Authors: Alessio Mazzetto, Eli Upfal
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove tight minimax risk bounds for both discrete and continuous smooth densities, where the minimum is over all possible estimates and the maximum is over all possible distributions that satisfy the drift constraints. Our technique handles a broad class of drift models and generalizes previous results on agnostic learning under drift. |
| Researcher Affiliation | Academia | Alessio Mazzetto 1 Eli Upfal 1 1Brown University. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving training on specific datasets. It refers to "samples" from distributions but not named datasets with public access information. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments with specific setup details like hyperparameters or training configurations. |