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Found 29 papers, 0 papers with code
Kernel-based learning with guarantees for multi-agent applications
This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment.
Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop
In this paper, we address the consistency of the simplified refined instrumental variable method for continuous-time systems (SRIVC) and its closed-loop variant CLSRIVC when they are applied on data that is generated from a feedback loop.
Statistical Analysis of Block Coordinate Descent Algorithms for Linear Continuous-time System Identification
Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form.
Coherence-based Input Design for Sparse System Identification
The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation.
Identification of Additive Continuous-time Systems in Open and Closed-loop
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system.
Unraveling the Control Engineer's Craft with Neural Networks
In this paper, we present a sim2real, direct data-driven controller tuning approach, where the digital twin is used to generate input-output data and suitable controllers for several perturbations in its parameters.
DRCFS: Doubly Robust Causal Feature Selection
Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science.
On the Relation between Discrete and Continuous-time Refined Instrumental Variable Methods
The Refined Instrumental Variable method for discrete-time systems (RIV) and its variant for continuous-time systems (RIVC) are popular methods for the identification of linear systems in open-loop.
Decentralized diffusion-based learning under non-parametric limited prior knowledge
We study the problem of diffusion-based network learning of a nonlinear phenomenon, $m$, from local agents' measurements collected in a noisy environment.
An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification
This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling.
Parsimonious Identification of Continuous-Time Systems: A Block-Coordinate Descent Approach
The identification of electrical, mechanical, and biological systems using data can benefit greatly from prior knowledge extracted from physical modeling.
Optimal Transport for Correctional Learning
In this framework, an expert agent, referred to as the teacher, modifies the data used by a learning agent, known as the student, to improve its estimation process.
A Unified Approach to Differentially Private Bayes Point Estimation
Standard algorithms for differentially private estimation are based on adding an appropriate amount of noise to the output of a traditional point estimation method.
On state-space representations of general discrete-time dynamical systems
In this paper we establish that every (deterministic) non-autonomous, discrete-time, causal, time invariant system has a state-space representation, and discuss its minimality.
A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation
One of the most important problems in system identification and statistics is how to estimate the unknown parameters of a given model.
A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning
A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest.
Asymptotically Optimal Bandits under Weighted Information
We study the problem of regret minimization in a multi-armed bandit setup where the agent is allowed to play multiple arms at each round by spreading the resources usually allocated to only one arm.
Consistency Analysis of the Closed-loop SRIVC Estimator
The Consistency of the Closed-Loop Simplified Refined Instrumental Variable method for Continuous-time system (CLSRIVC) is analysed based on sampled data.
Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations
In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods.
Cooperative System Identification via Correctional Learning
We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-agent (student), but cannot directly transfer its knowledge to the student.
A Finite-Sample Deviation Bound for Stable Autoregressive Processes
In this paper, we study non-asymptotic deviation bounds of the least squares estimator in Gaussian AR($n$) processes.
Bayesian Model Selection for Change Point Detection and Clustering
We address the new problem of estimating a piece-wise constant signal with the purpose of detecting its change points and the levels of clusters.
Estimator Selection: End-Performance Metric Aspects
The separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood (ML) approach or a Bayesian estimator such as the minimum mean square error (MMSE).
Evaluation of Spectral Learning for the Identification of Hidden Markov Models
Often, when applied in practice, the parameters of these models have to be estimated.
Bayesian Learning for Low-Rank matrix reconstruction
We develop latent variable models for Bayesian learning based low-rank matrix completion and reconstruction from linear measurements.
How to monitor and mitigate stair-casing in l1 trend filtering
It is known that TV denoising suffers from the so-called stair-case effect, which leads to detecting false change points.
Approximate Regularization Path for Nuclear Norm Based H2 Model Reduction
This paper concerns model reduction of dynamical systems using the nuclear norm of the Hankel matrix to make a trade-off between model fit and model complexity.
Relevance Singular Vector Machine for low-rank matrix sensing
In this paper we develop a new Bayesian inference method for low rank matrix reconstruction.
On change point detection using the fused lasso method
In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances.
