Publications related to Semi-differentiability

PURIFY: a new approach to radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a m ...

Wiley-Blackwell2014

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

Fabio Nobile, Eleonora Musharbash

In this work we discuss the Dynamically Orthogonal (DO) approximation of time dependent partial differential equations with random data. The approximate solution is expanded at each time instant on a time dependent orthonormal basis in the physical domain ...

MATHICSE2014

Learning non-parametric basis independent models from point queries via low-rank methods

Volkan Cevher, Hemant Tyagi

We consider the problem of learning multi-ridge functions of the form f (x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l(2)-ball in R-d, g is twice continuously differentiable almost everywhere, and A is an element ...

Elsevier2014

Active Learning of Multi-Index Function Models

Volkan Cevher, Hemant Tyagi

We consider the problem of actively learning \textit{multi-index} functions of the form

f(x) = g(Ax)= \sum_{i=1}^k g_i(a_i^Tx)

from point evaluations of

f

. We assume that the function

f

is defined on an

\ell_2

-ball in

\Real^d

,

g

is twice contin ...

2012

Learning Ridge Functions With Randomized Sampling In High Dimensions

Volkan Cevher, Hemant Tyagi

We study the problem of learning ridge functions of the form f(x) = g(aT x), x ∈ ℝd, from random samples. Assuming g to be a twice continuously differentiable function, we leverage techniques from low rank matrix recovery literature to derive a uniform app ...

IEEE2012

An optimal first-order solver for the TV-$L_1$ optical flow problem

Pierre Vandergheynst, Gilles Puy, Emmanuel D'Angelo

In this study, we address the problem of computing efficiently a dense optical flow between two images under a total variation (TV) regularization and an

L_1

norm data fidelity constraint using a variational method. We build upon Nesterov's framework for ...

2010

Dynamic network loading: a differentiable model that derives link state distributions

Michel Bierlaire, Gunnar Flötteröd, Carolina Osorio Pizano

We present a dynamic network loading model that yields queue length distributions, accounts for spillbacks, and maintains a differentiable mapping from the dynamic demand on the dynamic queue lengths. The approach builds upon an existing stationary queuein ...

2010

Semi-differentiability

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PURIFY: a new approach to radio-interferometric imaging

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

Learning non-parametric basis independent models from point queries via low-rank methods

Active Learning of Multi-Index Function Models

Learning Ridge Functions With Randomized Sampling In High Dimensions

An optimal first-order solver for the TV-$L_1$ optical flow problem

Dynamic network loading: a differentiable model that derives link state distributions

Dynamic network loading: a differentiable model that derives link state distributions

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

PURIFY: a new approach to radio-interferometric imaging

Active Learning of Multi-Index Function Models

Learning non-parametric basis independent models from point queries via low-rank methods

Learning Ridge Functions With Randomized Sampling In High Dimensions

An optimal first-order solver for the TV-$L_1$ optical flow problem