# Abdelhak M. Zoubir

## Contact Details

NameAbdelhak M. Zoubir |
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## Pubs By Year |
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## Pub CategoriesComputer Science - Information Theory (5) Mathematics - Information Theory (5) Statistics - Machine Learning (4) Statistics - Methodology (4) Computer Science - Multiagent Systems (3) Statistics - Applications (2) Computer Science - Computer Vision and Pattern Recognition (2) Computer Science - Learning (2) Mathematics - Statistics (1) Statistics - Theory (1) Mathematics - Probability (1) Mathematics - Dynamical Systems (1) Mathematics - Optimization and Control (1) Computer Science - Artificial Intelligence (1) |

## Publications Authored By Abdelhak M. Zoubir

The problem of minimizing convex functionals of probability distributions is solved under the assumption that the density of every distribution is bounded from above and below. First, a system of sufficient and necessary first order optimality conditions, which characterize global minima as solutions of a fixed-point equation, is derived. Based on these conditions, two algorithms are proposed that iteratively solve the fixed-point equation via a block coordinate descent strategy. Read More

A distributed multi-speaker voice activity detection (DM-VAD) method for wireless acoustic sensor networks (WASNs) is proposed. DM-VAD is required in many signal processing applications, e.g. Read More

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Read More

Hyperspectral imaging is an important tool in remote sensing, allowing for accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a hyperspectral image rarely represents a single material, but rather a mixture of different spectra. HSU aims at estimating the pure spectra present in the scene of interest, referred to as endmembers, and their fractions in each pixel, referred to as abundances. Read More

We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. Read More

We present a sparse estimation and dictionary learning framework for compressed fiber sensing based on a probabilistic hierarchical sparse model. To handle severe dictionary coherence, selective shrinkage is achieved using a Weibull prior, which can be related to non-convex optimization with $p$-norm constraints for $0 < p < 1$. In addition, we leverage the specific dictionary structure to promote collective shrinkage based on a local similarity model. Read More

We propose a versatile framework that unifies compressed sampling and dictionary learning for fiber-optic sensing. It employs a redundant dictionary that is generated from a parametric signal model and establishes a relation to the physical quantity of interest. Imperfect prior knowledge is considered in terms of uncertain local an global parameters. Read More

A new robust and statistically efficient estimator for ARMA models called the bounded influence propagation (BIP) {\tau}-estimator is proposed. The estimator incorporates an auxiliary model, which prevents the propagation of outliers. Strong consistency and asymptotic normality of the estimator for ARMA models that are driven by independently and identically distributed (iid) innovations with symmetric distributions are established. Read More

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. Read More

Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. Read More

Inverse reinforcement learning (IRL) is the problem of recovering a system's latent reward function from observed system behavior. In this paper, we concentrate on IRL in homogeneous large-scale systems, which we refer to as swarms. We show that, by exploiting the inherent homogeneity of a swarm, the IRL objective can be reduced to an equivalent single-agent formulation of constant complexity, which allows us to decompose a global system objective into local subgoals at the agent-level. Read More

Glucometers present an important self-monitoring tool for diabetes patients and therefore must exhibit high accu- racy as well as good usability features. Based on an invasive, photometric measurement principle that drastically reduces the volume of the blood sample needed from the patient, we present a framework that is capable of dealing with small blood samples, while maintaining the required accuracy. The framework consists of two major parts: 1) image segmentation; and 2) convergence detection. Read More

The density band model proposed by Kassam for robust hypothesis testing is revisited in this paper. First, a novel criterion for the general characterization of least favorable distributions is proposed, which unifies existing results. This criterion is then used to derive an implicit definition of the least favorable distributions under band uncertainties. Read More

Multi-target tracking is an important problem in civilian and military applications. This paper investigates distributed multi-target tracking using autonomous sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. Read More

The minimax robust hypothesis testing problem for the case where the nominal probability distributions are subject to both modeling errors and outliers is studied in twofold. First, a robust hypothesis testing scheme based on a relative entropy distance is designed. This approach provides robustness with respect to modeling errors and is a generalization of a previous work proposed by Levy. Read More

Under some mild Markov assumptions it is shown that the problem of designing optimal sequential tests for two simple hypotheses can be formulated as a linear program. The result is derived by investigating the Lagrangian dual of the sequential testing problem, which is an unconstrained optimal stopping problem, depending on two unknown Lagrangian multipliers. It is shown that the derivative of the optimal cost function with respect to these multipliers coincides with the error probabilities of the corresponding sequential test. Read More

A robust minimax test for two composite hypotheses, which are determined by the neighborhoods of two nominal distributions with respect to a set of distances - called $\alpha-$divergence distances, is proposed. Sion's minimax theorem is adopted to characterize the saddle value condition. Least favorable distributions, the robust decision rule and the robust likelihood ratio test are derived. Read More

Minimax decentralized detection is studied under two scenarios: with and without a fusion center when the source of uncertainty is the Bayesian prior. When there is no fusion center, the constraints in the network design are determined. Both for a single decision maker and multiple decision makers, the maximum loss in detection performance due to minimax decision making is obtained. Read More