The Statistical Cost of Robust Kernel Hyperparameter Turning
Authors: Raphael Meyer, Christopher Musco
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the statistical complexity of kernel hyperparameter tuning in the setting of active regression under adversarial noise. We provide finite-sample guarantees for the problem, characterizing how increasing the complexity of the kernel class increases the complexity of learning kernel hyperparameters. |
| Researcher Affiliation | Academia | Raphael A. Meyer Tandon School of Engineering New York University Brooklyn, NY 11201 ram900@nyu.edu Christopher Musco Tandon School of Engineering New York University Brooklyn, NY 11201 cmusco@nyu.edu |
| Pseudocode | Yes | Algorithm 1 Time Point Sampling input: Kernel functions kµ1, . . . kµQ, non-negative function p(t) on [0, T] with known integral P = R T 0 p(t)dt, number of samples n. output: Times t1, . . . , tn [0, T], weights v1, . . . , vn, PSD matrices Kµ1, . . . , KµQ Cn n. Algorithm 2 Computing the Interpolant input: Time points t1, . . . , tn [0, T], weights v1, . . . , vn, PSD matrix Kµ Cn n, regularization parameter ε > 0. ouput: Reconstructed function y, represented implicitly |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on statistical complexity and sample guarantees. It does not use or specify any publicly available datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not discuss training, validation, or test splits, as it does not involve empirical experiments with datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and provides algorithms and proofs, but does not detail a concrete experimental setup with hyperparameters for an empirical evaluation. |