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Found 16 papers, 8 papers with code
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity.
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.
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
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.
A Deep Learning Approach for Generating Soft Range Information from RF Data
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements.
Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation
Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments.
Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
In particular, we present a Bayesian model for the generative process of the received waveform composed by latent variables for both range-related features and environment semantics.
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions.
Double-Weighting for Covariate Shift Adaptation
Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $\mathrm{p}_\text{tr}(x)$ and $\mathrm{p}_\text{te}(x)$ are different but the label conditionals coincide.
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification.
Minimax risk classifiers with 0-1 loss
Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability).
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.
Probabilistic Load Forecasting Based on Adaptive Online Learning
Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand.
Minimax Classification with 0-1 Loss and Performance Guarantees
We also present MRCs' finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w. r. t.
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.
Supervised classification via minimax probabilistic transformations
Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss.
General Supervision via Probabilistic Transformations
Different types of training data have led to numerous schemes for supervised classification.
