arXiv research

Cyclic coordinate descent identifies models in finite time and converges linearly.

Study on identifying and inferring nonlinear dynamics on unknown networks.

Proposes SPCA to incorporate structural constraints in model 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…

ADSGD method speeds up model identification in sparse optimization.

Consistent partial identification of causal effects proved for neural models.

Deep SSMs use neural networks to identify complex systems.

A tutorial on non-asymptotic system identification methods.

Simplified identification methods for causal inference with arbitrary interventional distributions.

Bayesian methods reduce variance in subspace identification for small data sets.

Paper introduces a conformer-based system for streaming language identification in long-form speech.

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…

Bayesian neural networks with nonparametric noise models for system identification.

We establish a connection between trend filtering and system identification which results in a family of new identification methods for linear, time-varying (LTV) dynamical models based on convex optimization. We demonstrate how the design of the cost function promotes a model with either a continuous change in dynamic…

dynoGP uses deep Gaussian processes for dynamic system identification.

This paper tackles Bayesian system identification with probabilistic numerical methods.

CNN identifies nonlinear human posture control models efficiently.

Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures …

Semi-parametric framework for nonlinear system identification

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

This paper addresses the identification of insurance models with multidimensional screening where insurees have private information about their risk and risk aversion. The model includes a random damage and the possibility of several claims. Screening of insurees relies on their certainty equivalence. The paper then in…

We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identifica…

Bayesian method improves online NARMAX model identification.

A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stab…

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

A method for identifying NPWARX models with arbitrary domains using probabilistic mixture models.

This work is devoted to modelling and identification of the dynamics of the inter-sectoral balance of a macroeconomic system. An approach to the problem of specification and identification of a weakly formalized dynamical system is developed. A matching procedure for parameters of a linear stationary Cauchy problem wit…

The paper explores how missing data problems are related to causal inference.

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

dynoNet learns dynamical systems using linear operators.

Paper proposes a new PLL framework with a progressive identification algorithm.

Meta-learners estimate CATE from multiple environments with partial identification.

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

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability a…

The paper offers error bounds for quantized dynamical models.

Volterra series are especially useful for nonlinear system identification, also thanks to their capability to approximate a broad range of input-output maps. However, their identification from a finite set of data is hard, due to the curse of dimensionality. Recent approaches have shown how regularized kernel-based met…

Paper unifies subspace identification and DMD for dynamical systems.

Challenge hides and seeks privacy in clinical time-series data.

New method identifies diffusion sources on networks with statistical confidence.

An adversarial process between two deep neural networks is a promising approach to train a robust model. In this paper, we propose an adversarial process using cosine similarity, whereas conventional adversarial processes are based on inverted categorical cross entropy (CCE). When used for training an identification mo…

Paper identifies unobserved variables from observable data.

IIC decouples causal identification into two phases, significantly reducing the HTC gap in linear SEMs.

BINDy uses Bayesian methods to identify nonlinear dynamics from data.

Study on generating and identifying languages privately, showing privacy imposes costs and creates barriers.

Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism e…

New method improves neural network robustness by identifying functions rather than parameters.

PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.

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