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Found 14 papers, 6 papers with code
Physics-informed machine learning as a kernel method
In this context, we consider a general regression problem where the empirical risk is regularized by a partial differential equation that quantifies the physical inconsistency.
An analysis of the noise schedule for score-based generative models
Under mild assumptions on the data distribution, we establish an upper bound for the KL divergence between the target and the estimated distributions, explicitly depending on any time-dependent noise schedule.
Random features models: a way to study the success of naive imputation
Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data.
Sparse tree-based initialization for neural networks
Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data.
Minimax rate of consistency for linear models with missing values
Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys...).
Model-based Clustering with Missing Not At Random Data
In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.
Debiasing Averaged Stochastic Gradient Descent to handle missing values
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.
Analyzing the tree-layer structure of Deep Forests
Random forests on the one hand, and neural networks on the other hand, have met great success in the machine learning community for their predictive performance.
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates.
Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithms to real-world datasets.
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Considering a data matrix generated from a probabilistic principal component analysis (PPCA) model containing several MNAR variables, not necessarily under the same self-masked missing mechanism, we propose estimators for the means, variances and covariances of the variables and study their consistency.
Statistics Theory Statistics Theory
Imputation and low-rank estimation with Missing Not At Random data
Our second contribution is to suggest a computationally efficient surrogate estimation by implicitly taking into account the joint distribution of the data and the missing mechanism: the data matrix is concatenated with the mask coding for the missing values; a low-rank structure for exponential family is assumed on this new matrix, in order to encode links between variables and missing mechanisms.
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model.
On oracle-type local recovery guarantees in compressed sensing
We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing.
Information Theory Information Theory
