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Found 26 papers, 8 papers with code
Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
We study the problem of estimating the optimal Q-function of $\gamma$-discounted Markov decision processes (MDPs) under the synchronous setting, where independent samples for all state-action pairs are drawn from a generative model at each iteration.
Model Updating for Nonlinear Systems with Stability Guarantees
First, we introduce a method to extend an existing known model with an uncertainty model so that stability of the extended model is guaranteed in the sense of set invariance and input-to-state stability.
Uncertainty Learning for LTI Systems with Stability Guarantees
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.
Robust Multivariate Detection and Estimation with Fault Frequency Content Information
Next, shifting attention to fault estimation in specific frequency ranges, an exact reformulation of the optimal estimation filter design using the restricted Hinf performance index is derived, which is inherently non-convex.
Inverse Optimization for Routing Problems
We propose a method for learning decision-makers' behavior in routing problems using Inverse Optimization (IO).
Nonlinear Distributionally Robust Optimization
We then propose a G-derivative based Frank-Wolfe (FW) algorithm for generic nonlinear optimization problems in probability spaces and establish its convergence under the proposed notion of smoothness in a completely norm-independent manner.
Robust Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
We provide sufficient conditions that guarantee stability of the estimation error dynamics: firstly, asymptotic stability (i. e., exact fault estimation) in the absence of perturbations induced by the fault model mismatch (mismatch between internal ultra-local model for the fault and the actual fault dynamics), 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.
Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms
In Inverse Optimization (IO), an expert agent solves an optimization problem parametric in an exogenous signal.
Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems
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.
Fast Algorithm for Constrained Linear Inverse Problems
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.
Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
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.
Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments
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.
Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction
In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems.
Multimode Diagnosis for Switched Affine Systems with Noisy Measurement
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.
Principal Component Hierarchy for Sparse Quadratic Programs
We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix.
The Nonconvex Geometry of Linear Inverse Problems
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.
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
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.
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
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.
Learning robust control for LQR systems with multiplicative noise via policy gradient
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.
Wasserstein Distributionally Robust Kalman Filtering
Despite the non-convex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program.
Distance Based Source Domain Selection for Sentiment Classification
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.
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
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.
Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
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.
Regularization via Mass Transportation
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.
Generalized maximum entropy estimation
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.
Distributionally Robust Logistic Regression
This paper proposes a distributionally robust approach to logistic regression.
