Search Results for author:
Found 13 papers, 6 papers with code
PAC-Bayes-Chernoff bounds for unbounded losses
This result can be understood as a PAC-Bayesian version of the Cram\'er-Chernoff bound.
Time-dependent Probabilistic Generative Models for Disease Progression
We propose a Markovian generative model of treatments developed to (i) model the irregular time intervals between medical events; (ii) classify treatments into subtypes based on the patient sequence of medical events and the time intervals between them; and (iii) segment treatments into subsequences of disease progression patterns.
Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems
However, these algorithms require a large number of evaluations to provide a quality solution, which might be computationally expensive when the evaluation cost is high.
Efficient Learning of Minimax Risk Classifiers in High Dimensions
High-dimensional data is common in multiple areas, such as health care and genomics, where the number of features can be tens of thousands.
Dirichlet process mixture models for non-stationary data streams
In recent years, we have seen a handful of work on inference algorithms over non-stationary data streams.
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences
In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images.
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common.
MRCpy: A Library for Minimax Risk Classifiers
MRCpy provides a unified interface for different variants of MRCs and follows the standards of popular Python libraries.
K-means for Evolving Data Streams
In this paper, we formally define the Streaming $K$-means(S$K$M) problem, which implies a restart of the error function when a concept drift occurs.
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.
Candidate Labeling for Crowd Learning
In this paper, the use of candidate labeling for crowd learning is proposed, where the annotators may provide more than a single label per instance to try not to miss the real label.
An efficient K -means clustering algorithm for massive data
The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields.
An efficient K-means algorithm for Massive Data
Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to ma- nipulate and analyze such information.
