111 citations · 132 across the 14 of their papers we have counts for
19 papers
IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing
Yuxiao Li, Keke Hu, Bobai Zhao +2
Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep le…
A Deep Generative Model for Synthesizing Labeled Wireless Signals
Yuxiao Li, Keke Hu, Santiago Mazuelas +1
Wireless signals with position-related labels are pivotal for both performance evaluation and model training in the realm of wireless sensing. However, acquiring real-world dataset…
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…
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such c…
Minimax risk classifiers with 0-1 loss
Santiago Mazuelas, Mauricio Romero, Peter Grünwald
Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability). Conventional methods enable tractable lea…
MRCpy: A Library for Minimax Risk Classifiers
Kartheek Bondugula, Verónica Álvarez, José I. Segovia-Martín +2
Libraries for supervised classification have enabled the wide-spread usage of machine learning methods. Existing libraries, such as scikit-learn, caret, and mlpack, implement techn…
Probabilistic Load Forecasting Based on Adaptive Online Learning
Verónica Álvarez, Santiago Mazuelas, José A. Lozano
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevan…
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…
Generalized Maximum Entropy for Supervised Classification
Santiago Mazuelas, Yuan Shen, Aritz Pérez
The maximum entropy principle advocates to evaluate events' probabilities using a distribution that maximizes entropy among those that satisfy certain expectations' constraints. Su…
Supervised classification via minimax probabilistic transformations
Santiago Mazuelas, Andrea Zanoni, Aritz Perez
Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss. Favored families of classi…
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…
On the Performance Limits of Map-Aware Localization
Francesco Montorsi, Santiago Mazuelas, Giorgio M. Vitetta +1
Establishing bounds on the accuracy achievable by localization techniques represents a fundamental technical issue. Bounds on localization accuracy have been derived for cases in w…
Cooperative Network Navigation: Fundamental Limit and its Geometrical Interpretation
Yuan Shen, Santiago Mazuelas, Moe Z. Win
Localization and tracking of moving nodes via network navigation gives rise to a new paradigm, where nodes exploit both temporal and spatial cooperation to infer their positions ba…