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

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Forward-Backward Splitting for Optimal Transport Based Problems

Pascal Frossard, Guillermo Ortiz Jimenez, Mireille El Gheche, Hermina Petric Maretic, Effrosyni Simou

Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regula ...

2020

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Michaël Unser

Regularization addresses the ill-posedness of the training problem in machine learning or the reconstruction of a signal from a limited number of measurements. The method is applicable whenever the problem is formulated as an optimization task. The standar ...

2020

Evolutionary Algorithm Optimization of Staggered Biological or Biomimetic Composites Using the Random Fuse Model

Roberto Guarino, Gianluca Costagliola, Federico Bosia

In Nature, biological materials such as nacre, bone, and dentin display an enhanced mechanical strength due to their structure characterized by hard inclusions embedded in a soft matrix. This structure has inspired the design of artificial materials with o ...

2020

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Fernando Porté Agel, Roberto Brogna

Layout optimization of wind farms constitutes an important and challenging task in complex terrain. This is especially due to the complex interactions of the boundary layer flows in complex terrain and wind turbine wakes, which renders wake modelling in co ...

ELSEVIER SCI LTD2020

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Timothy Michael Hillel, Virginie Janine Camille Lurkin, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2020

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Loris Silva Brites

The assessment of the risk maps for the seismic vulnerability at large scale is based on the vulnerability of each building. In order to determine these vulnerabilities, it is first required to assign to each building its construction class. The constructi ...

2020

Second-Order Guarantees In Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which generally giv ...

IEEE2020

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

Marco Matter

In daily adaptive proton therapy (DAPT), the treatment plan is re-optimized on a daily basis. It is a straightforward idea to incorporate information from the previous deliveries during the optimization to refine this daily proton delivery. A feedback sign ...

2020

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Florent Gérard Krzakala, Lenka Zdeborová

Despite the widespread use of gradient-based algorithms for optimizing high-dimensional non-convex functions, understanding their ability of finding good minima instead of being trapped in spurious ones remains to a large extent an open problem. Here we fo ...

Curran Associates, Inc.2020

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

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Convergence of adaptive algorithms for constrained weakly convex optimization

Forward-Backward Splitting for Optimal Transport Based Problems

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Evolutionary Algorithm Optimization of Staggered Biological or Biomimetic Composites Using the Random Fuse Model

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Second-Order Guarantees In Federated Learning

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Convergence of adaptive algorithms for constrained weakly convex optimization

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Evolutionary Algorithm Optimization of Staggered Biological or Biomimetic Composites Using the Random Fuse Model

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Second-Order Guarantees In Federated Learning

Forward-Backward Splitting for Optimal Transport Based Problems

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval