Robust Density Estimation under Besov IPM Losses
Authors: Ananya Uppal, Shashank Singh, Barnabas Poczos
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study minimax convergence rates of nonparametric density estimation under the Huber contamination model... We provide the first results for this problem under a large family of losses... We show that a re-scaled thresholding wavelet estimator converges at minimax optimal rates... All theoretical results are proven in the Appendix. |
| Researcher Affiliation | Collaboration | Ananya Uppal Department of Mathematical Sciences Carnegie Mellon University auppal@andrew.cmu.edu Shashank Singh Machine Learning Department Carnegie Mellon University shashanksi@google.com Barnabás Póczos Machine Learning Department Carnegie Mellon University bapoczos@cs.cmu.edu |
| Pseudocode | No | The paper describes the estimators mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention specific datasets, public or otherwise, for training models. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, therefore no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no specific 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, therefore no specific experimental setup details like hyperparameters or training configurations are provided. |