4 citations · 5 across the 5 of their papers we have counts for
6 papers
Rank-One Signal Recovery in Sparse Wishart Noise
Preben Forer, Urte Adomaityte, Pierpaolo Vivo
We study the high-dimensional recovery of a signal vector in the presence of sparse Wishart-like noise. We define an matrix $A = J+(θ/N)\mathbf{xx}^{\top}…
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…
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
Urte Adomaityte, Leonardo Defilippis, Bruno Loureiro +1
We investigate the high-dimensional properties of robust regression estimators in the presence of heavy-tailed contamination of both the covariates and response functions. In parti…
Unveiling the Hessian's Connection to the Decision Boundary
Mahalakshmi Sabanayagam, Freya Behrens, Urte Adomaityte +1
Understanding the properties of well-generalizing minima is at the heart of deep learning research. On the one hand, the generalization of neural networks has been connected to the…
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…
Planted matching problems on random hypergraphs
Urte Adomaityte, Anshul Toshniwal, Gabriele Sicuro +1
We consider the problem of inferring a matching hidden in a weighted random -hypergraph. We assume that the hyperedges' weights are random and distributed according to two diffe…