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The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
MALA is a popular gradient-based Markov chain Monte Carlo method to access the Gibbs-posterior distribution. Stochastic MALA (sMALA) scales to large data sets, but changes the target distribution from the Gibbs-posterior to a surrogate posterior which only exploits a reduced sample size. We introduce a corrected stochastic MALA (csMALA) with a simple correction term for which di...

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Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?
Training deep learning neural networks often requires massive amounts of computational ressources. We propose to sequentially monitor network predictions to trigger retraining only if the predictions are no longer valid. This can reduce drastically computational costs and opens a door to green deep learning. Our approach is based on the relationship to projected second moments m...

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Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors
In the framework of the FD (frequent directions) algorithm, we first develop two efficient algorithms for low-rank matrix approximations under the embedding matrices composed of the product of any SpEmb (sparse embedding) matrix and any standard Gaussian matrix, or any SpEmb matrix and any SRHT (subsampled randomized Hadamard transform) matrix. The theoretical results are also a...

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Identifying Weight-Variant Latent Causal Models
The task of causal representation learning aims to uncover latent higher-level causal variables that affect lower-level observations. Identifying the true latent causal variables from observed data, while allowing instantaneous causal relations among latent variables, remains a challenge, however. To this end, we start with the analysis of three intrinsic indeterminacies in iden...

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Classification Under Local Differential Privacy with Model Reversal and Model Averaging
Local differential privacy has become a central topic in data privacy research, offering strong privacy guarantees by perturbing user data at the source and removing the need for a trusted curator. However, the noise introduced by local differential privacy often significantly reduces data utility. To address this issue, we reinterpret private learning under local differential p...

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