no code implementations • 31 Oct 2023 • Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
We propose a methodology to extend the dynamics of an LTI (without uncertainty) with an uncertainty model, based on measured data, to improve the predictive capacity of the model in the input-output sense.
no code implementations • 7 Oct 2023 • Jingwei Dong, Kaikai Pan, Sergio Pequito, Peyman Mohajerin Esfahani
This paper studies the problem of fault detection and estimation (FDE) for LTI systems with a particular focus on frequency content information for the faults, possibly as a continuum range, and under both disturbances and stochastic noise.
1 code implementation • 14 Jul 2023 • Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
We propose a method for learning decision-makers' behavior in routing problems using Inverse Optimization (IO).
no code implementations • 5 Jun 2023 • Mohammed Rayyan Sheriff, Peyman Mohajerin Esfahani
Motivated by this, we propose an alternative notion for the derivative and corresponding smoothness based on Gateaux (G)-derivative for generic risk measures.
no code implementations • 23 May 2023 • Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
We provide sufficient conditions that guarantee stability of the estimation error dynamics: firstly, asymptotic stability (i. e., perfect fault estimation) in the absence of perturbations induced by fault model mismatch (mismatch between internal, ultralocal model for the fault and the actual fault characteristics), uncertainty, external disturbances, and measurement noise and, secondly, Input-to-State Stability (ISS) of the estimation error dynamics is guaranteed in the presence of these perturbations.
1 code implementation • 12 May 2023 • Pedro Zattoni Scroccaro, Bilge Atasoy, Peyman Mohajerin Esfahani
In Inverse Optimization (IO), an expert agent solves an optimization problem parametric in an exogenous signal.
no code implementations • 24 Apr 2023 • Jingwei Dong, Yucheng Liao, Haiwei Xie, Jochen Cremer, Peyman Mohajerin Esfahani
In this paper, we propose a data-assisted diagnosis scheme based on an optimization-based fault detection filter with the output current as the only measurement.
1 code implementation • 2 Dec 2022 • Mohammed Rayyan Sheriff, Floor Fenne Redel, Peyman Mohajerin Esfahani
We consider the constrained Linear Inverse Problem (LIP), where a certain atomic norm (like the $\ell_1 $ norm) is minimized subject to a quadratic constraint.
no code implementations • 11 Nov 2022 • Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults -- leading to an approximated linear model in the augmented state.
no code implementations • 1 May 2022 • Pedro Zattoni Scroccaro, Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani
In the past few years, Online Convex Optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees.
no code implementations • 4 Apr 2022 • Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems.
no code implementations • 21 Oct 2021 • Jingwei Dong, Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani
We study a diagnosis scheme to reliably detect the active mode of discrete-time, switched affine systems in the presence of measurement noise and asynchronous switching.
1 code implementation • 25 May 2021 • Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani
We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix.
no code implementations • 7 Jan 2021 • Armin Eftekhari, Peyman Mohajerin Esfahani
The gauge function, closely related to the atomic norm, measures the complexity of a statistical model, and has found broad applications in machine learning and statistical signal processing.
1 code implementation • 8 Nov 2019 • Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani
The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator -- that is, a measurable function of the observation -- and a fictitious adversary choosing a prior -- that is, a pair of signal and noise distributions ranging over independent Wasserstein balls -- with the goal to minimize and maximize the expected squared estimation error, respectively.
no code implementations • 23 Aug 2019 • Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh
The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples.
1 code implementation • 28 May 2019 • Benjamin Gravell, Peyman Mohajerin Esfahani, Tyler Summers
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuous state and action spaces.
1 code implementation • NeurIPS 2018 • Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani
Despite the non-convex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program.
no code implementations • 28 Aug 2018 • Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani
The performance of the proposed methodology is validated through an SC case study in which our numerical experiments suggest a significant improvement in the cross domain classification error in comparison with a random selected source domain for both a naive and adaptive learning setting.
no code implementations • ICML 2018 • Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamas Keviczky
Ordinary differential equations, and in general a dynamical system viewpoint, have seen a resurgence of interest in developing fast optimization methods, mainly thanks to the availability of well-established analysis tools.
no code implementations • 18 May 2018 • Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a $p$-dimensional Gaussian random vector from $n$ independent samples.
1 code implementation • 27 Oct 2017 • Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani
The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution.
no code implementations • 24 Aug 2017 • Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros
We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise.
no code implementations • NeurIPS 2015 • Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn
This paper proposes a distributionally robust approach to logistic regression.