Search Results for author:
Found 21 papers, 5 papers with code
Kernel KMeans clustering splits for end-to-end unsupervised decision trees
Trees are convenient models for obtaining explainable predictions on relatively small datasets.
Are Ensembles Getting Better all the Time?
More precisely, in that case, the average loss of the ensemble is a decreasing function of the number of models.
Generalised Mutual Information: a Framework for Discriminative Clustering
In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations.
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models
In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels, which occur when some classes are more likely to be labeled than others.
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection
Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters.
Explainability as statistical inference
The model parameters can be learned via maximum likelihood, and the method can be adapted to any predictor network architecture and any type of prediction problem.
Generalised Mutual Information for Discriminative Clustering
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations.
A Multi-stage deep architecture for summary generation of soccer videos
The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries which are similar enough but with relevant variability to provide different options to the final editor.
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Our debiasing approach is straightforward to implement and applicable to most deep SSL methods.
Model-agnostic out-of-distribution detection using combined statistical tests
These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentiable generative model.
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives
Inspired by this simple monotonicity theorem, we present a series of nonasymptotic results that link properties of Monte Carlo estimates to tightness of MCOs.
How to deal with missing data in supervised deep learning?
To address supervised deep learning with missing values, we propose to marginalize over missing values in a joint model of covariates and outcomes.
Tensor decomposition for learning Gaussian mixtures from moments
In data processing and machine learning, an important challenge is to recover and exploit models that can represent accurately the data.
Unobserved classes and extra variables in high-dimensional discriminant analysis
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase.
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference.
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries.
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
Our approach, called MIWAE, is based on the importance-weighted autoencoder (IWAE), and maximises a potentially tight lower bound of the log-likelihood of the observed data.
Leveraging the Exact Likelihood of Deep Latent Variable Models
Finally, we describe an algorithm for missing data imputation using the exact conditional likelihood of a deep latent variable model.
Exact Dimensionality Selection for Bayesian PCA
We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset.
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
To this end, using Roweis' probabilistic interpretation of PCA and a Gaussian prior on the loading matrix, we provide the first exact computation of the marginal likelihood of a Bayesian PCA model.
