Publications related to Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Material removing tool and method for designing the same

Jean-François Molinari, Son-Jonathan Pham-Ba

The invention relates to a material removing tool, such as an ice cream scoop, and method for designing the same. The material removing tool has a shape optimized to minimize the amount of work the user has to provide in order to form and remove a piece of ...

2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

François Maréchal, Ivan Daniel Kantor, Julia Granacher

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...

2021

Computational Inverse Design of Surface-based Inflatables

Mark Pauly, Florin Isvoranu, Tian Chen

We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves ...

ASSOC COMPUTING MACHINERY2021

Automated Design: A Journey Across Modelling, Optimization, and Education

Cyril Picard

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...

EPFL2021

Large Steps in Inverse Rendering of Geometry

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to mi ...

ASSOC COMPUTING MACHINERY2021

Geometric optimization of dielectric elastomer electrodes for dynamic applications

Herbert Shea, Bekir Aksoy, Emil Yan Garnell

Dielectric elastomers are soft actuators, made of an elastomer membrane sandwiched by compliant electrodes. Because of their high energy density and quick response, they are well suited for dynamic applications such as loudspeakers. Thanks to progress in t ...

ELSEVIER SCI LTD2021

A Newton Frank-Wolfe method for constrained self-concordant minimization

Volkan Cevher, Quoc Tran Dinh

We develop a new Newton Frank-Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds gl ...

SPRINGER2021

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Volkan Cevher, Ali Kavis

In this work we investigate stochastic non-convex optimization problems wherethe objective is an expectation over smooth loss functions, and the goal is to find an approximate stationary point. The most popular approach to handling such problems is varianc ...

2021

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Volkan Cevher, Shaul Nadav Hallak

This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

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Material removing tool and method for designing the same

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Computational Inverse Design of Surface-based Inflatables

Automated Design: A Journey Across Modelling, Optimization, and Education

Large Steps in Inverse Rendering of Geometry

Geometric optimization of dielectric elastomer electrodes for dynamic applications

A Newton Frank-Wolfe method for constrained self-concordant minimization

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Material removing tool and method for designing the same

Computational Inverse Design of Surface-based Inflatables

Automated Design: A Journey Across Modelling, Optimization, and Education

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Geometric optimization of dielectric elastomer electrodes for dynamic applications

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Large Steps in Inverse Rendering of Geometry

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

A Newton Frank-Wolfe method for constrained self-concordant minimization