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Found 14 papers, 0 papers with code
Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case
Sign-Perturbed Sum (SPS) is a powerful finite-sample system identification algorithm which can construct confidence regions for the true data generating system with exact coverage probabilities, for any finite sample size.
Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands
The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees.
On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
Here, we study the problem under much milder assumptions: in addition to the standard Lipschitz and margin conditions, a novel characteristic of the absolutely continuous component is introduced, by which the exact convergence rate of the classification error probability is calculated, both for the binary and for the multi-label cases.
Robust Independence Tests with Finite Sample Guarantees for Synchronous Stochastic Linear Systems
The paper introduces robust independence tests with non-asymptotically guaranteed significance levels for stochastic linear time-invariant systems, assuming that the observed outputs are synchronous, which means that the systems are driven by jointly i. i. d.
Distribution-Free Inference for the Regression Function of Binary Classification
One of the key objects of binary classification is the regression function, i. e., the conditional expectation of the class labels given the inputs.
Recursive Estimation of Conditional Kernel Mean Embeddings
In this paper we present a new recursive algorithm to estimate the conditional kernel mean map in a Hilbert space valued $L_2$ space, that is in a Bochner space.
Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables
The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system identification method for the identification of closed-loop observable stochastic linear systems in state-space form.
Nonparametric, Nonasymptotic Confidence Bands with Paley-Wiener Kernels for Band-Limited Functions
The paper introduces a method to construct confidence bands for bounded, band-limited functions based on a finite sample of input-output pairs.
Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings
In this paper we suggest two statistical hypothesis tests for the regression function of binary classification based on conditional kernel mean embeddings.
Semi-Parametric Uncertainty Bounds for Binary Classification
The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs.
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods.
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models
A standard model of (conditional) heteroscedasticity, i. e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance.
Wireless Multi-Sensor Networks for Smart Cities: A Prototype System with Statistical Data Analysis
As urbanization proceeds at an astonishing rate, cities have to continuously improve their solutions that affect the safety, health and overall wellbeing of their residents.
Adaptive Stochastic Resource Control: A Machine Learning Approach
The paper investigates stochastic resource allocation problems with scarce, reusable resources and non-preemtive, time-dependent, interconnected tasks.
