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Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate ...
The role of asymmetric prediction losses in smart charging of electric vehicles
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 ...
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address ...
Generalized Maximum Entropy for Supervised Classification
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 ...
A Deep Learning Approach for Generating Soft Range Information from RF Data
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 ...
Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework
The overall global death rate for COVID-19 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 ...