arXiv research

Paper explores using EEG for better speaker identification, even in noisy environments.

Bayesian neural networks with nonparametric noise models for system identification.

This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.

New method identifies network dynamics and noise structure.

This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based…

Unified bounds for DP risks reduce noise and improve accuracy.

Improved SGD bounds for machine learning models with Markovian noise.

The paper tackles drift identification in Lévy α-stable stochastic systems, proposing a Fourier space approach.

Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.

A new clustering algorithm fuses heat diffusion and turning angle for robustness.

Optimal noise excitation for linear system identification reduces sample complexity.

This paper solves matrix blind joint block diagonalization with noise.

WSINDy for PDEs robustly identifies models from noisy data.

The paper identifies generators of linear SDEs with noise types.

Improved SINDy autoencoder for identifying noisy dynamical systems.

Bayesian method improves forecasting of nonseparable Hamiltonian systems with noise.

End-to-end algorithm for controlling bilinear systems with probabilistic noise.

There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extracted in probability forms. The biggest obstacle of the probability model is the probability distribu…

Using integration by parts on Gaussian space we construct a Stein Unbiased Risk Estimator (SURE) for the drift of Gaussian processes using their local and occupation times. By almost-sure minimization of the SURE risk of shrinkage estimators we derive an estimation and de-noising procedure for an input signal perturbed…

Method improves SINDy for noisy nonlinear systems.

We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise. There, we present a robust method for the prediction of the outputs of the LDS and identification of the samples of noise, and prove guarantees on its statistical performance. One app…

Paper reduces sample complexity for bilinear systems identification to nearly constant.

Bayesian method improves online NARMAX model identification.

New techniques improve the accuracy of identifying nonlinear systems from noisy data.

A new clustering method for vector time series using autoregressive dynamics.

Study non-parametric frequency-domain system identification from finite samples.

CNN identifies nonlinear human posture control models efficiently.

This study examines how noise levels affect causal discovery methods.

This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is special in that…

This paper tackles Bayesian system identification with probabilistic numerical methods.

NSIBF detects anomalies in CPS using neural system identification and Bayesian filtering.

Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robust…

Study mini-batch SGD noise and its limits, proving complexity guarantees.

Paper addresses state estimation in sensor networks with intermittent data.

The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this work, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formu…

In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential …

SINDy-PI robustly identifies implicit dynamics from noisy data.

WSINDy algorithm proves robust to noise in identifying differential equations.

The paper provides a non-asymptotic error bound for linear system identification under nonlinear policies.

We propose a new method for blind system identification. Resorting to a Gaussian regression framework, we model the impulse response of the unknown linear system as a realization of a Gaussian process. The structure of the covariance matrix (or kernel) of such a process is given by the stable spline kernel, which has b…

New bounds quantify estimation error in kernel-based system identification with unknown hyperparameters.

This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structure thus used as a benchmark to promote further research. With identifiability of the network guaranteed, this paper presents an identificatio…

A new test detects noise in graph data, useful for forecasting.

In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalm…

Unified Bayesian framework for LTV system identification using neural networks and Gaussian Processes.

Paper shows faster core identification in matching markets.

Sparse Bayesian learning algorithm for estimating interaction kernels in Motsch-Tadmor model.

We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structu…