Publications associées à Cost curve

Log Barriers for Safe Non-convex Black-box Optimization

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

We address the problem of minimizing a smooth function f0(x) over a compact set D defined by smooth functional constraints fi(x)≤0, i=1,…,m given noisy value measurements of fi(x). This problem arises in safety-critical applications, where certain paramete ...

2019

Optimal design of biomass supply chains with integrated process design

François Maréchal, Theodoros Damartzis

The current work focuses on the optimal design of biomass supply chains, addressing the conversion of raw materials into useful services. The latter include not only heat and power but also synthetic bio-based fuels (e.g. bio-SNG). Optimally pre-designed p ...

Elsevier2019

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Rachid Guerraoui, El Mahdi El Mhamdi, Julien Michel Stainer, Peva François Blanchard

The present application concerns a computer-implemented method for training a machine learning model in a distributed fashion, using Stochastic Gradient Descent, SGD, wherein the method is performed by a first computer in a distributed computing environmen ...

2019

Incorporating Location Aspects in Process Integration Methodology

François Maréchal, Ivan Daniel Kantor, Hür Ebuzer Bütün

The large potential for waste resource and heat recovery in industry has been motivating research toward increasing efficiency. Process integration methods have proven to be effective tools in improving industrial sites while decreasing their resource and ...

2019

Adaptive damage retention mechanism enables healthier yeast population

Riccardo Dainese

During cytokinesis in budding yeast (Saccharomyces cerevisiae) damaged proteins are distributed asymmetrically between the daughter and the mother cell. Retention of damaged proteins is a crucial mechanism ensuring a healthy daughter cell with full replica ...

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD2019

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Pascal Frossard, Arun Venkitaraman

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

Value Creation in Precision Medicine: Acknowledging the Need to Devise New Ways to Measure. The Value of Healthcare

Anjali Devi Vanapilli Nursimulu

Precision medicine holds the promise of bringing the right treatment to the right patient at the right time with the upside of reduced undesirable effects. But the impact on healthcare costs are not clear. While targeting of conventional drugs through prec ...

International Risk Governance Center (IRGC)2019

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

Mario Paolone, Mokhtar Bozorg, Mostafa Nick

In this paper we present an adaptive robust optimization framework for the day-ahead scheduling of Active Distribution Networks (ADNs) where the controlled devices are distributed Energy Storage Systems (ESSs). First, the targeted problem is formulated usi ...

IEEE2019

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Cost curve

Log Barriers for Safe Non-convex Black-box Optimization

Optimal design of biomass supply chains with integrated process design

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Incorporating Location Aspects in Process Integration Methodology

Nash and Wardrop Equilibria in Aggregative Games With Coupling Constraints

Adaptive damage retention mechanism enables healthier yeast population

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Value Creation in Precision Medicine: Acknowledging the Need to Devise New Ways to Measure. The Value of Healthcare

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

A Representer Theorem for Deep Neural Networks

Graph Chatbot

Chattez avec Graph Search

Adaptive damage retention mechanism enables healthier yeast population

A Representer Theorem for Deep Neural Networks

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Nash and Wardrop Equilibria in Aggregative Games With Coupling Constraints

Log Barriers for Safe Non-convex Black-box Optimization

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

Optimal design of biomass supply chains with integrated process design

Incorporating Location Aspects in Process Integration Methodology

Value Creation in Precision Medicine: Acknowledging the Need to Devise New Ways to Measure. The Value of Healthcare