Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Density Estimation under Besov IPM Losses
Authors: Ananya Uppal, Shashank Singh, Barnabas Poczos
NeurIPS 2020 | Venue PDF | 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 EMAIL Shashank Singh Machine Learning Department Carnegie Mellon University EMAIL Barnabás Póczos Machine Learning Department Carnegie Mellon University EMAIL |
| 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. |