111 citations · 132 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Double-Weighting for Covariate Shift Adaptation
José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu
Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates ) of training and testing samples $\mathrm{p}_\text{tr}(x…
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Yuxiao Li, Santiago Mazuelas, Yuan Shen
Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. L…
Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
Yuxiao Li, Santiago Mazuelas, Yuan Shen
Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line-of-sig…
A Deep Learning Approach for Generating Soft Range Information from RF Data
Yuxiao Li, Santiago Mazuelas, Yuan Shen
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range informati…
Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation
Yuxiao Li, Santiago Mazuelas, Yuan Shen
Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging…
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
Yuxiao Li, Santiago Mazuelas, Yuan Shen
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains chall…