2 citations · 3 across the 17 of their papers we have counts for
20 papers
Differentiating Minimal-Norm Solutions to Parametric Optimization Problems
Baptiste Plaquevent-Jourdain, Jalal Fadili, Antonio Silveti-Falls
Differentiating through parametric optimization problems is central to bilevel programming and meta-learning, often accomplished using approximate implicit differentiation. The imp…
Second order splitting dynamics for stochastic monotone inclusions with closed loop distribution
Wutao Si, Hamza Ennaji, Jalal Fadili
In this paper, we investigate the problem of finding a zero of the sum of a maximal monotone operator and a cocoercive operator $\Bm$ in a Hilbert space. This formulation natur…
The Iterates of Nesterov's Accelerated Algorithm Converge in The Critical Regimes
Radu Ioan Bot, Jalal Fadili, Dang-Khoa Nguyen
In this paper, we prove that the iterates of the accelerated Nesterov's algorithm in the critical regime do converge in the weak topology to a global minimizer of an -smooth fun…
Inexact and Stochastic Gradient Optimization Algorithms with Inertia and Hessian Driven Damping
Harsh Choudhary, Jalal Fadili, Vyachelav Kungurtsev
In a real Hilbert space setting, we study the convergence properties of an inexact gradient algorithm featuring both viscous and Hessian driven damping for convex differentiable op…
Convergence rates of regularized quasi-Newton methods without strong convexity
Shida Wang, Jalal Fadili, Peter Ochs
In this paper, we study convergence rates of the cubic regularized proximal quasi-Newton method (\csr) for solving non-smooth additive composite problems that satisfy the so-called…
Global non-asymptotic super-linear convergence rates of regularized proximal quasi-Newton methods on non-smooth composite problems
Shida Wang, Jalal Fadili, Peter Ochs
In this paper, we propose two regularized proximal quasi-Newton methods with symmetric rank-1 update of the metric (SR1 quasi-Newton) to solve non-smooth convex additive composite…
Stochastic Monotone Inclusion with Closed Loop Distributions
Hamza Ennaji, Jalal Fadili, Hedy Attouch
In this paper, we study in a Hilbertian setting, first and second-order monotone inclusions related to stochastic optimization problems with decision dependent distributions. The s…
Inertial Methods with Viscous and Hessian driven Damping for Non-Convex Optimization
Rodrigo Maulen-Soto, Jalal Fadili, Peter Ochs
In this paper, we aim to study non-convex minimization problems via second-order (in-time) dynamics, including a non-vanishing viscous damping and a geometric Hessian-driven dampin…
An SDE Perspective on Stochastic Inertial Gradient Dynamics with Time-Dependent Viscosity and Geometric Damping
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch +1
Our approach is part of the close link between continuous dissipative dynamical systems and optimization algorithms. We aim to solve convex minimization problems by means of stocha…
The stochastic Ravine accelerated gradient method with general extrapolation coefficients
Hedy Attouch, Jalal Fadili, Vyacheslav Kungurtsev
In a real Hilbert space domain setting, we study the convergence properties of the stochastic Ravine accelerated gradient method for convex differentiable optimization. We consider…
Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
Nathan Buskulic, Jalal Fadili, Yvain Quéau
Advanced machine learning methods, and more prominently neural networks, have become standard to solve inverse problems over the last years. However, the theoretical recovery guara…
Stochastic Inertial Dynamics Via Time Scaling and Averaging
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch +1
Our work is part of the close link between continuous-time dissipative dynamical systems and optimization algorithms, and more precisely here, in the stochastic setting. We aim to…
Tikhonov Regularization for Stochastic Non-Smooth Convex Optimization in Hilbert Spaces
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch
To solve convex optimization problems with a noisy gradient input, we analyze the global behavior of subgradient-like flows under stochastic errors. The objective function is compo…
Solution uniqueness of convex optimization problems via the radial cone
Jalal Fadili, Tran T. A. Nghia, Duy Nhat Phan
In this paper, we mainly study solution uniqueness of some convex optimization problems. Our characterizations of solution uniqueness are in terms of the radial cone. This approach…
Accelerated Gradient Dynamics on Riemannian Manifolds: Faster Rate and Trajectory Convergence
Tejas Natu, Camille Castera, Jalal Fadili +1
In order to minimize a differentiable geodesically convex function, we study a second-order dynamical system on Riemannian manifolds with an asymptotically vanishing damping term o…
SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems
Sidney Besnard, Frédéric Jurie, Jalal M. Fadili
This paper introduces a novel approach to solve inverse problems by leveraging deep learning techniques. The objective is to infer unknown parameters that govern a physical system…
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
Nathan Buskulic, Jalal Fadili, Yvain Quéau
Neural networks have become a prominent approach to solve inverse problems in recent years. While a plethora of such methods was developed to solve inverse problems empirically, we…
Limits and consistency of non-local and graph approximations to the Eikonal equation
Jalal Fadili, Nicolas Forcadel, Thi Tuyen Nguyen +1
In this paper, we study a non-local approximation of the time-dependent (local) Eikonal equation with Dirichlet-type boundary conditions, where the kernel in the non-local problem…
Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms
Peter Ochs, Jalal Fadili, Thomas Brox
We propose a unifying algorithm for non-smooth non-convex optimization. The algorithm approximates the objective function by a convex model function and finds an approximate (Bregm…
The Degrees of Freedom of the Group Lasso
Samuel Vaiter, Charles Deledalle, Gabriel Peyré +2
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overdetermined linear regression model. Such a regularization is known to promote spa…