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Efficient Learning of Minimax Risk Classifiers in High Dimensions 

Bondugula, K.R.Autoridad BCAM; Mazuelas, S.Autoridad BCAM; Pérez, A.Autoridad BCAM (2023-08-01)
High-dimensional data is common in multiple areas, such as health care and genomics, where the number of features can be tens of thousands. In such scenarios, the large number of features often leads to inefficient ...
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Minimax Risk Classifiers with 0-1 Loss 

Mazuelas, S.Autoridad BCAM; Romero, M.; Grunwald, P. (2023-07-01)
Supervised classification techniques use training samples to learn a classification rule with small expected 0 -1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample ...
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A Variational Learning Approach for Concurrent Distance Estimation and Environmental Identification 

Li, Y; Mazuelas, S.Autoridad BCAM; Shen, Y. (2023-02-01)
Wireless propagated signals encapsulate rich information for high-accuracy localization and environment sensing. However, the full exploitation of positional and environmental features as well as their correlation remains ...
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Female Models in AI and the Fight Against COVID-19 

Guerrero, C.Autoridad BCAM; Mazuelas, S.Autoridad BCAM (2022-11-01)
Gender imbalance has persisted over time and is well documented in science, technology, engineering and mathematics (STEM) and singularly in artificial intelligence (AI). In this article we emphasize the importance of ...
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The role of asymmetric prediction losses in smart charging of electric vehicles 

Straka, M.; Buzna, L.; Refa, N.; Mazuelas, S.Autoridad BCAM (2022-07-01)
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 ...
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Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees 

Alvarez, V.; Mazuelas, S.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2022-07)
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 ...
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Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables 

li, Y.; Mazuelas, S.Autoridad BCAM; Shen, Y. (2022-05-23)
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 ...
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Generalized Maximum Entropy for Supervised Classification 

Mazuelas, S.Autoridad BCAM; Shen, Y.; Pérez, A.Autoridad BCAM (2022-04)
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 ...
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A Deep Learning Approach for Generating Soft Range Information from RF Data 

li, Y.; Mazuelas, S.Autoridad BCAM; Shen, Y. (2022-01-24)
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 ...
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Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework 

Armañanzas, R.Autoridad BCAM; Diaz, A.Autoridad BCAM; Martinez, M.Autoridad BCAM; Mazuelas, S.Autoridad BCAM (2022-01-14)
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 ...
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Mazuelas, S. (22)
Shen, Y. (8)Conti, A. (4)Pérez, A. (4)Bartoletti, S. (3)li, Y. (2)Lozano, J.A. (2)Morselli, F. (2)Win, M. (2)Win, M.Z. (2)... másSubjectsupervised classification (2)Asymmetric loss function (1)Bayesian framework (1)Bayesian Model (1)conditional generative problems (1)COVID-19, Cost-Sensitive Prognosis, Clinical Indicator, Multistart Estimation (1)Deep Generative Model (1)domain adaptation (1)Electric vehicles (1)Environment Identification, (1)... másFecha2023 (3)2022 (7)2021 (4)2020 (3)2019 (3)2018 (2)

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