arXiv research

Paper accelerates nonlinear mapping in online systems with lower time complexity.

A distributed system identification method for LTI systems using reverse experience replay.

Online algorithm identifies PDEs from noisy data snapshots.

The Duffing oscillator's parameters are identified online using variational message passing.

This work compares data reduction criteria for online Gaussian Processes.

Bayesian method improves online NARMAX model identification.

AdaptOn achieves logarithmic regret in adaptive control of unknown partially observable linear systems.

There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and …

Improved SGD bounds for machine learning models with Markovian noise.

Paper proposes online learning for estimating AC network admittance matrix.

Greedy policy maximizes information in unknown linear systems.

An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample …

This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…

Bayesian method for multivariate autoregressive models with exogenous inputs.

New method estimates bidirectional causal effects in large-scale systems.

Efficiently controls unknown linear systems with black-box interactions.

Owing to the rapid growth of touchscreen mobile terminals and pen-based interfaces, handwriting-based writer identification systems are attracting increasing attention for personal authentication, digital forensics, and other applications. However, most studies on writer identification have not been satisfying because …

Paper addresses online identification and clustering for mixed linear regression models.

Paper achieves logarithmic regret for online Kalman filter learning.

GP-NODE combines Gaussian processes and NeuralODEs for Bayesian system identification.

New algorithm optimizes linear system estimation from single trajectory.

CyPhERS provides real-time event info for CPSs, avoiding downtime.

We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l1-l2 norm regularizer. By systematically treating the non-differentiable regulariz…

Paper tackles dynamic graph topology identification in time-varying graphs.

Online algorithms for identifying river pollution sources.

It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification …

Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers. Recently, the Robust PCA approach via nuclear norm minimization has been extended to matrices with linear structures which appear in applications such as system identificat…

We consider online detection strategies for identifying a change point in a stream of quantum particles allegedly prepared in identical states. We show that the identification of the change point can be done without error via sequential local measurements while attaining the optimal performance bound set by quantum mec…

Study non-asymptotic BPI guarantees for online RL.

Purpose: Malicious web domain identification is of significant importance to the security protection of Internet users. With online credibility and performance data, this paper aims to investigate the use of machine learning tech-niques for malicious web domain identification by considering the class imbalance issue (i…

Efficiently identifies best policies in tabular MDPs with reduced computational cost.

Efficiently identifies promising hyperparameters for online learning models.

A fundamental problem in control is to learn a model of a system from observations that is useful for controller synthesis. To provide good performance guarantees, existing methods must assume that the real system is in the class of models considered during learning. We present an iterative method with strong guarantee…

In the present paper we study interval identification systems of order three. We prove that the Rauzy induction preserves symmetry: for any symmetric interval identification system of order three after finitely many iterations of the Rauzy induction we always obtain a symmetric system. We also provide an example of sym…

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

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…

dynoGP uses deep Gaussian processes for dynamic system identification.

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…

New model-free algorithm achieves similar LQR regret guarantees.

Bayesian neural networks with nonparametric noise models for system identification.

The paper explores when linear system identification is hard or easy, especially for under-actuated systems.

Study identifies and validates a method for system identification of Markov jump linear systems.

This paper tackles Bayesian system identification with probabilistic numerical methods.

Deep SSMs use neural networks to identify complex systems.

A tutorial on non-asymptotic system identification methods.

We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal $\ell_{1,\infty}$-penalized recursive least squares (R…

Semi-parametric framework for nonlinear system identification

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two int…