Course/seminar

MIDAS-PRO-LASSO: Mixed Frequency Data Regression Models with Parameter Profiling and LASSO

Jonas Striaukas, CORE UCLouvain

The Effect of Loyalty in  the Kirman and Vriend (2001) Fish Market Model

Dávid Kopányi, Universiteit van Amsterdam

(Joint work with Alex Possajennikov)

V. Filipe Martins-da-Rocha

(Université Paris Dauphine)

will give a presentatin on

Self-enforcing Debt Limits and Costly Default in General Equilibrium

Abstract :

De nombreux développements liés à l'intelligence artificielle posent de nouvelles questions aux juristes. Cette journée abordera les questions pour juristes, donnera des outils pour praticiens et alimentera les réflexions prospectives

Gert Peersman

(Ghent University)

will give a presentation on

Risk trading in capacity expansion models

Gauthier De Maere, ENGIE

The talk presents a set of power investment models, the class of risky capacity equilibrium problems, reflecting different assumptions of perfect and imperfect markets. The models are structured in a unified stochastic Nash game framework. Each model is the concatenation of a model of the short-term market operations (perfect competition or Cournot), with a long-term model of investment behavior (risk neutral and risk averse behavior under different assumptions of risk trading).

Faster algorithms for (regularized) Optimal Transport

Pavel Dvurechensky, Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany

Dual subgradient method with averaging for optimal resource allocation

Vladimir Shikhman, Chemnitz University

Increasing stability of training of Deep CNNs with Stochastic Gradient Descent method. Application to Image classification tasks

Jenny Benois-Pineau, University of Bordeaux

Supervised learning for classification of visual data has been the major approach during the last two decades. With adventure of GPUs, the well-known supervised classifiers such as Artificial Neural Networks (ANN) came up for these problems.

Multi-agent decision-making in uncertain environments

Maryam Kamgarpour, ETH Zurich