Publications associées à Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management

Publications associées (18)

Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

Recent years have witnessed a growing integration of automatic control into a wide range of cyber-physical systems, including electrical grids and autonomous transportation networks. These systems operate in uncertain and dynamic environments, exhibit comp ...

EPFL2025

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization

Rui Yao

Deep neural networks (DNNs) have been increasingly applied in travel demand modeling because of their automatic feature learning, high predictive performance, and economic interpretability. Nevertheless, DNNs frequently present behaviorally irregular patte ...

PERGAMON-ELSEVIER SCIENCE LTD2024

On Linear Optimization over Wasserstein Balls

Daniel Kuhn, Wolfram Wiesemann

Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distributionally robust optimization and machine learning communities to formulate an ...

2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Sadegh Farhadkhani, John Stephan

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

PMLR2022

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Emmanuel Abbé

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

ASSOC COMPUTING MACHINERY2022

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

IEEE2022

MILP-based discrete sizing and topology optimization of truss structures: new formulation and benchmarking

Corentin Jean Dominique Fivet, Gennaro Senatore, Jan Friedrich Georg Brütting

Discrete sizing and topology optimization of truss structures subject to stress and displacement constraints has been formulated as a Mixed-Integer Linear Programming (MILP) problem. The computation time to solve a MILP problem to global optimality via a b ...

2022

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

A unified framework for Hamiltonian deep neural networks

Giancarlo Ferrari Trecate, Liang Xu, Clara Lucía Galimberti

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

2021

AC OPF in radial distribution networks – Part II: An augmented Lagrangian-based OPF algorithm, distributable via primal decomposition

Mario Paolone, Jean-Yves Le Boudec, Dan-Cristian Tomozei, Konstantina Christakou

In the first part of this two-part paper we show that the branch-flow convexification of the OPF problem is not exact and that the ADMM-based decomposition of the OPF fails to converge in specific scenarios. Therefore, there is a need to develop algorithms ...

Elsevier2017

A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill

François Maréchal, Maziar Kermani

An optimization methodology based on Mixed Integer Linear Programming (MILP) has been developed for simultaneous optimization of water and energy (SOWE) in industrial processes. The superstructure integrates non-water process thermal streams and optimizes ...

2017

A parametric augmented Lagrangian algorithm for real-time economic NMPC

Colin Neil Jones, Jean-Hubert Hours, Harsh Ambarishkumar Shukla

In this paper, a novel optimality-tracking algorithm for solving Economic Nonlinear Model Predictive Control (ENMPC) problems in real-time is presented. Developing online schemes for ENMPC is challenging, since it is unclear how convexity of the Quadratic ...

IEEE2016

A Tutorial on Machine Learning in Educational Science

Pierre Dillenbourg, Lukasz Kidzinski

Popularity of massive online open courses (MOOCs) allowed educational researchers to address problems which were not accessible few years ago. Although classical statistical techniques still apply, large datasets allow us to discover deeper patterns and to ...

Springer-Verlag Singapore Pte Ltd2016

Control and Optimization of Batch Processes

Dominique Bonvin

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch counter. Accordingly, the control and opt ...

Springer Science+Business Media2015

Synthesis and Optimization of Pipelines for HW Implementations of Dataflow Programs

Marco Mattavelli, Endri Bezati, Ab Al Hadi Bin Ab Rahman, Anatoly Prihozhy

This paper introduces a new methodology for pipeline synthesis with applications to data flow high-level system design. The pipeline synthesis is applied to dataflow programs whose operators are translated into graphs and dependencies relations that are th ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Graph-based denoising for time-varying point clouds

Yann Mikaël Schoenenberger, Pierre Vandergheynst, Johann Paratte

Noisy 3D point clouds arise in many applications. They may be due to errors when creating a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information betw ...

2015

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches

Jean-Philippe Thiran, Xavier Bresson, Dominique Zosso

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the da ...

Ieee-Inst Electrical Electronics Engineers Inc2014