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 [1].
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels
Authors: Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present the first known lower bounds for the estimation of MMD based on finite samples. Our lower bounds hold for any radial universal kernel on Rd and match the existing upper bounds up to constants that depend only on the properties of the kernel. Using these lower bounds, we establish the minimax rate optimality of the empirical estimator and its U-statistic variant, which are usually employed in applications. |
| Researcher Affiliation | Academia | Ilya Tolstikhin Department of Empirical Inference MPI for Intelligent Systems Tübingen 72076, Germany EMAIL Bharath K. Sriperumbudur Department of Statistics Pennsylvania State University University Park, PA 16802, USA EMAIL Bernhard Schölkopf Department of Empirical Inference MPI for Intelligent Systems Tübingen 72076, Germany EMAIL |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs; no pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code or providing links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve experimental data or datasets, thus no information on public datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not involve any computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is purely theoretical and does not involve any computational experiments, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setups, hyperparameters, or training configurations. |