Publications related to Handheld reflectance acquisition of paintings

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Learning without Smoothness and Strong Convexity

Yen-Huan Li

Recent advances in statistical learning and convex optimization have inspired many successful practices. Standard theories assume smoothness---bounded gradient, Hessian, etc.---and strong convexity of the loss function. Unfortunately, such conditions may ...

EPFL2018

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

Martin Vetterli, Thierry Blu, Hanjie Pan

It is a classic problem to estimate continuous-time sparse signals, like point sources in a direction-of-arrival problem, or pulses in a time-of-flight measurement. The earliest occurrence is the estimation of sinusoids in time series using Prony's method. ...

Institute of Electrical and Electronics Engineers2017

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coord ...

2017

Riemannian Optimization For High-Dimensional Tensor Completion

Michael Maximilian Steinlechner

Tensor completion aims to reconstruct a high-dimensional data set where the vast majority of entries is missing. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem rest ...

Siam Publications2016

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Vassily Hatzimanikatis, Ljubisa Miskovic, Stefano Andreozzi

Information about kinetics and enzyme activities while essential for quantitative understanding of metabolic dynamics remains rarely available and always involves uncertainty. In this work, we introduce the first computational methodology capable of determ ...

2016

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

MATHICSE Technical Report : Riemannian optimization for high-dimensional tensor completion

Michael Maximilian Steinlechner

Tensor completion aims to reconstruct a high-dimensional data set with a large fraction of missing entries. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem restricte ...

MATHICSE2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Handheld reflectance acquisition of paintings

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Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Learning without Smoothness and Strong Convexity

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

Safe Adaptive Importance Sampling

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Riemannian Optimization For High-Dimensional Tensor Completion

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Shapes from Pixels

MATHICSE Technical Report : Riemannian optimization for high-dimensional tensor completion

Random sampling of bandlimited signals on graphs

Shapes from Pixels

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Riemannian Optimization For High-Dimensional Tensor Completion

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

MATHICSE Technical Report : Riemannian optimization for high-dimensional tensor completion

Random sampling of bandlimited signals on graphs

Learning without Smoothness and Strong Convexity

Safe Adaptive Importance Sampling