74 citations · 429 across the 31 of their papers we have counts for
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Sparse corruption in low-rank matrix inference: the PCA benchmark
Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo
Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations. It is known that applying PCA to a rank-one signal corrupted by a de…
Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo
We characterise the learning of a mixture of two clouds of data points with generic centroids via empirical risk minimisation in the high dimensional regime, under the assumptions…
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
Bruno Loureiro, Cédric Gerbelot, Maria Refinetti +2
From the sampling of data to the initialisation of parameters, randomness is ubiquitous in modern Machine Learning practice. Understanding the statistical fluctuations engendered b…
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot +3
Generalised linear models for multi-class classification problems are one of the fundamental building blocks of modern machine learning tasks. In this manuscript, we characterise t…