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Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a costeffective enhancement in the computational ability of hardwareconstrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approaches are put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. By exploiting both temporal and contextual information, two novel BO approaches, termed time-varying BO and contextual time-varying BO, are developed. Numerical tests validate the merits of the proposed BO approaches compared with contemporary benchmarks under different MEC network sizes. 

Partial correlations (PCs) and the related inverse covariance matrix adopted by graphical lasso, are widely applicable tools for learning graph connectivity given nodal observations. The resultant estimators however, can be sensitive to outliers. Robust approaches developed so far to cope with outliers do not (explicitly) account for nonlinear interactions possibly present among nodal processes. This can hurt the identification of graph connectivity, merely due to model mismatch. To overcome this limitation, a novel formulation of robust PC is introduced based on nonlinear kernel functions. The proposed scheme leverages robust ridge regression techniques, spectral Fourier feature based kernel approximants, and robust association measures. Numerical tests on synthetic and real data illustrate the potential of the novel approach. 

Over-the-air federated learning (OTA-FL) is a communication-effective approach for achieving distributed learning tasks. In this paper, we aim to enhance OTA-FL by seamlessly combining sensing into the communication-computation integrated system. Our research reveals that the wireless waveform used to convey OTA-FL parameters possesses inherent properties that make it well-suited for sensing, thanks to its remarkable auto-correlation characteristics. By leveraging the OTA-FL learning statistics, i.e., means and variances of local gradients in each training round, the sensing results can be embedded therein without the need for additional time or frequency resources. Finally, by considering the imperfections of learning statistics that are neglected in the prior works, we end up with an optimized the transceiver design to maximize the OTA-FL performance. Simulations validate that the proposed method not only achieves outstanding sensing performance but also significantly lowers the learning error bound. 

Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions regarding the emergence of HOLs, or, they rely on reduced dimensionality models of limited expressiveness. To overcome these limitations, the HOLP approach developed here leverages distribution similarities across embeddings as captured by a learnable probability metric. The intuition underpinning the novel approach is that sets of nodes whose embeddings are less similar in distribution, are less likely to be connected by a HOL. Specifically, nonlinear dimensionality reduction is effected through a Gaussian process latent variable model that yields nodal embeddings, and also learns a data-driven similarity function (kernel). This kernel forms the core of a maximum mean discrepancy probability metric. Tests on benchmark datasets illustrate the potential of the proposed approach. 

Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics. BO hinges on a Bayesian surrogate model to sequentially select query points so as to balance exploration with exploitation of the search space. Most existing works rely on a single Gaussian process (GP) based surrogate model, where the kernel function form is typically preselected using domain knowledge. To bypass such a design process, this paper leverages an ensemble (E) of GPs to adaptively select the surrogate model fit on-the-fly, yielding a GP mixture posterior with enhanced expressiveness for the sought function. Acquisition of the next evaluation input using this EGP-based function posterior is then enabled by Thompson sampling (TS) that requires no additional design parameters. To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model. The novel EGP-TS readily accommodates parallel operation. To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret for both sequential and parallel settings. Tests on synthetic functions and real-world applications showcase the merits of the proposed method. 

Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a costeffective enhancement in the computational ability of hardware-constrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approach is put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, by exploiting temporal information, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. Numerical tests validate the effectiveness of the proposed BO approach. 

Graph-guided semi-supervised learning (SSL) has gained popularity in several network science applications, including biological, social, and financial ones. SSL becomes particularly challenging when the available nodal labels are scarce, what motivates naturally the active learning (AL) paradigm. AL seeks the most informative nodes to label in order to effectively estimate the nodal values of unobserved nodes. It is also referred to as active sampling, and boils down to learning the sought function mapping, and an acquisition function (AF) to identify the next node(s) to sample. To learn the mapping, this work leverages an adaptive Bayesian model comprising an ensemble (E) of Gaussian Processes (GPs) with enhanced expressiveness of the function space. Unlike most alternatives, the EGP model relies only on the one-hop connectivity of each node. Capitalizing on this EGP model, a suite of novel and intuitive AFs are developed to guide the active sampling process. These AFs are then combined with weights that are adapted incrementally to further robustify performance. Numerical tests on real and synthetic datasets corroborate the merits of the novel methods. 

Massive datasets are typically distributed geographically across multiple sites, where scalability, data privacy and integrity, as well as bandwidth scarcity typically discourage uploading these data to a central server. This has propelled the so-called federated learning framework where multiple workers exchange information with a server to learn a “centralized” model using data locally generated and/or stored across workers. This learning framework necessitates workers to communicate iteratively with the server. Although appealing for its scalability one needs to carefully address the various data distribution shifts across workers, which degrades the performance of the learnt model. In this context, the distributionally robust optimization framework is considered here. The objective is to endow the trained model with robustness against adversarially manipulated input data, or, distributional uncertainties, such as mismatches between training and testing data distributions, or among datasets stored at different workers. To this aim, the data distribution is assumed unknown, and to land within a Wasserstein ball centered around the empirical data distribution. This robust learning task entails an infinite-dimensional optimization problem, which is challenging. Leveraging a strong duality result, a surrogate is obtained, for which a primal-dual algorithm is developed. Compared to classical methods, the proposed algorithm offers robustness with little computational overhead. Numerical tests using image datasets showcase the merits of the proposed algorithm under several existing adversarial attacks and distributional uncertainties.