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It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust t...
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...
Robust stability of a linear multivariable system, in the sense of robustness under multiplicative transfer function perturbation, is necessarily preserved under sufficiently small perturbations in th...
A method of computing the finite-horizon control inputs for FIR plants whose parameters are only known to lie in a set is proposed. The parameter set is assumed to be described by an ellipsoidal bound...
Presents a control design method for continuous-time plants whose uncertain parameters in the output matrix are only known to lie in an ellipsoidal set. The desired control is chosen to minimize the m...
Gives some vague ideas about useful control design tools that might be based on LMI algorithms.
This paper describes how to efficiently solve a robust optimal control problem using recently developed primal-dual interior-point methods. Among potential applications are model predictive control. T...
We describe an efficient method for solving an optimal control problem that arises in robust model-predictive control. The problem is to design the input sequence that minimizes the peak tracking erro...
This paper introduces an extension of minimum variance beamforming, also known as Capon's method, that explicitly takes into account variation or uncertainty in the assumed array response. Sources of ...
We consider the array signal processing problem of choosing the weight vector to minimize noise power, subject to a unit array gain for the desired wave, and subject to rejection constraints on interf...
In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensuring a required perform...
We consider a moral hazard problem in which the principal has a slight uncertainty about how the agent’s actions translate into output. An incentive contract can be made robust against an ǫ amou...
An automated procedure for optimization of proportional–integral–derivative (PID)-type controller parameters for single-input, single-output (SISO) plants with explicit model uncertainty is presente...
We propose a computational approach to systematically find decentralized H-infinity suboptimal controllers for general unstructured models. Exploiting the quadratically invariant model projection and ...
In this paper we address the problem of estimating the attitudes and positions of a set of cameras in an external coordinate system. Starting from a conventional global structure-from-motion pipeline,...

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