10 accepted papers
Detecting the origin of information or infection spread in networks is a fundamental challenge with applications in misinformation tracking, epidemiology, and beyond. We study the multi-source detection problem: given snapshot observations of node infection status on a graph, estimate the set of sou
A plethora of recent works has shown that for wide, overparameterized neural networks, training with Stochastic Gradient Descent (SGD) often leads to interpolation of the training data without sacrificing generalization performance. A key parameter that is not only closely connected to generalizatio…
We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. For matrix factorization problems, this phenomenon has been studied in a number of works. A particular challenge has been to design universal initialization…
"Establishing certified uncertainty quantification (UQ) in imaging processing applications continues to pose a significant challenge. In particular, such a goal is crucial for accurate and reliable medical imaging if one aims for precise diagnostics and appropriate intervention. In the case of magne…
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor. We develop a new data-driven approach for UQ in regression that applies both to classical optimization approaches such as the LASSO as well as…
One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -minimization. The initial works focused on re…
In contrast to Shannon sampling theory, where measurements are recorded at equally-spaced time instants, event-driven sampling records values at non-uniform instants dependent on the input. However, both sampling schemes are subject to input dynamic range constraints. This represents a fundamental b…
The fractional Fourier transform has found applications in a variety of topics linked with science and engineering. In this context, sampling theory is one of the most well-studied subjects. Since the fractional Fourier transform or the FrFT generalizes the notion of bandlimitedness, extension of Sh…
Conventional analog-to-digital converters (ADCs) are limited in dynamic range. If a signal exceeds some prefixed threshold, the ADC saturates and the resulting signal is clipped, thus becoming prone to aliasing artifacts. Recent developments in ADC design allow to overcome this limitation: using mod…
In a recent paper [1], we introduced the concept of “Unlimited Sampling”. This unique approach circumvents the clipping or saturation problem in conventional analog-to-digital converters (ADCs) by considering a radically different ADC architecture which resets the input voltage before saturation. Su…