1 citations · 1 across the 9 of their papers we have counts for
10 papers
Optimal Transport PAC-Bayes Bounds
Armin Beck, Peter Ochs, Jalal Fadili
PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in terms of the Kullback-Leibler (K…
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…
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…
From Learning to Optimize to Learning Optimization Algorithms
Camille Castera, Peter Ochs
Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not…
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
Michael Sucker, Jalal Fadili, Peter Ochs
We use the PAC-Bayesian theory for the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable…
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…
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…
Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization
Severin Maier, Camille Castera, Peter Ochs
We introduce an autonomous system with closed-loop damping for first-order convex optimization. While, to this day, optimal rates of convergence are almost exclusively achieved by…