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Supervised Learning in Time-dependent Environments with Performance Guarantees
Álvarez, V.
(2023-09-25)
(2023-09-25)
In practical scenarios, it is common to learn from a sequence of related problems (tasks).
Such tasks are usually time-dependent in the sense that consecutive tasks are often
significantly more similar. Time-dependency ...
Efficient Learning of Minimax Risk Classifiers in High Dimensions
Bondugula, K.R.; Mazuelas, S.; Pérez, A. (2023-08-01)
; Mazuelas, S.; Pérez, A. (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 ...
New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem
Benavides, X.; Ceberio, J.; Hernando, L.; Lozano, J.A. (2023-07-15)
; Ceberio, J.; Hernando, L.; Lozano, J.A. (2023-07-15)
Previous works have shown that studying the characteristics of the Quadratic Assignment Problem (QAP) is a crucial step in gaining knowledge that can be used to design tailored meta-heuristic algorithms. One way to analyze ...
Minimax Risk Classifiers with 0-1 Loss
Mazuelas, S.; Romero, M.; Grunwald, P. (2023-07-01)
; 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 ...
Time-Varying Lyapunov Control Laws with Enhanced Estimation of Distribution Algorithm for Low-Thrust Trajectory Design
Shirazi, A.; Holt, H.; Armellin, R.; Baresi, N. (2023-04-30)
; Holt, H.; Armellin, R.; Baresi, N. (2023-04-30)
Enhancements in evolutionary optimization techniques are rapidly growing in many aspects of engineering, specifically in astrodynamics and space trajectory optimization and design. In this chapter, the problem of optimal ...
A Variational Learning Approach for Concurrent Distance Estimation and Environmental Identification
Li, Y; Mazuelas, S.; Shen, Y. (2023-02-01)
; 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 ...
Learning a logistic regression with the help of unknown features at prediction stage
Martinez, M.; Inza, I.; Lozano, J.A. (2023)
; Inza, I.; Lozano, J.A. (2023)
The use of features available at training time, but not
at prediction time, as additional information for training models
is known as learning using privileged information paradigm. In
this paper, the handling of ...
Learning the progression patterns of treatments using a probabilistic generative model
Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)
; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)
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 ...
Female Models in AI and the Fight Against COVID-19
Guerrero, C.; Mazuelas, S. (2022-11-01)
; Mazuelas, S. (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 ...
Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-10-09)
; Ceberio, J.; Lozano, J.A. (2022-10-09)
In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs ...