12 citations · 21 across the 5 of their papers we have counts for
7 papers
Efficient Learning of Minimax Risk Classifiers in High Dimensions
Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez
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 f…
K-means for Evolving Data Streams
Arkaitz Bidaurrazaga, Aritz Pérez, Marco Capó
Currently the amount of data produced worldwide is increasing beyond measure, thus a high volume of unsupervised data must be processed continuously. One of the main unsupervised d…
Minimax Classification with 0-1 Loss and Performance Guarantees
Santiago Mazuelas, Andrea Zanoni, Aritz Perez
Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-samp…
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Etor Arza, Aritz Perez, Ekhine Irurozki +1
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivat…
Rank aggregation for non-stationary data streams
Ekhine Irurozki, Jesus Lobo, Aritz Perez +1
We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the…
General Supervision via Probabilistic Transformations
Santiago Mazuelas, Aritz Perez
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle gener…