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Learning the progression patterns of treatments using a probabilistic generative model
(20221215)Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model ... 
Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques
(20221009)In this paper, a direct approach is developed for discovering optimal transfer trajectories of closerange rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs ... 
AdHoc Explanation for Time Series Classification
(2022)In this work, a perturbationbased modelagnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ... 
Learning a Battery of COVID19 Mortality Prediction Models by Multiobjective Optimization
(20220709)The COVID19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the ... 
The role of asymmetric prediction losses in smart charging of electric vehicles
(20220701)Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires ... 
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
(202207)The statistical characteristics of instancelabel pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate ... 
An active adaptation strategy for streaming time series classification based on elastic similarity measures
(20220521)In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In online ... 
Variational Bayesian Framework for Advanced Image Generation with DomainRelated Variables
(20220523)Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for generalpurpose generative modeling of data distributions. However, it remains challenging for existing methods to address ... 
Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
(20211207)Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and nonline ofsight conditions. This paper proposes a ... 
Implementing the Cumulative Difference Plot in the IOHanalyzer
(202207)The IOHanalyzer is a webbased framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results ... 
A Resource Sharing Game for the Freshness of Status Updates
(20210901) 
Rank aggregation for nonstationary data streams
(2022)The problem of learning over nonstationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the nonstationarity means that the distribution ... 
A Deep Learning Approach for Generating Soft Range Information from RF Data
(20220124)Radio frequency (RF)based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative ... 
Time Series Classifier Recommendation by a MetaLearning Approach
(20220326)This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or metatargets: classifier accuracies, complete ranking, topM ranking, best set ... 
Analysis of Dominant Classes in Universal Adversarial Perturbations
(2022)The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ ent strategies can be employed to efficiently generate adversarial attacks, some ... 
Generalized Maximum Entropy for Supervised Classification
(202204)The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ... 
Kmeans for Evolving Data Streams
(20210101)Nowadays, streaming data analysis has become a relevant area of research in machine learning. Most of the data streams available are unlabeled, and thus it is necessary to develop specific clustering techniques that take ... 
Derivation of a CostSensitive COVID19 Mortality Risk Indicator Using a Multistart Framework
(20220114)The overall global death rate for COVID19 patients has escalated to 2.13% after more than a year of worldwide spread. Despite strong research on the infection pathogenesis, the molecular mechanisms involved in a fatal ... 
A SemiSupervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
(20211230)Localization systems based on ultrawide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of nonlineofsight (NLOS) errors. Learningbased methods for error ... 
Deep GEMbased network for weakly supervised UWB ranging error mitigation
(20211230)Ultrawideband (UWB)based techniques, while becoming mainstream approaches for highaccurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learningbased methods for error ...