All Years Seminars
[INMA] 2018-03-20 (14:00) : Distributed Energy Resource Control and Network Optimization
At Euler building (room A.002)
Speaker :
Duncan Callaway (University of California, Berkeley)
Abstract :
In this talk I'll discuss theory, simulation and experimental work aimed at understanding how distributed energy resources (DERs; such as batteries, flexible loads and photovoltaic generators) impact distribution networks and how these impacts can be managed. I will also briefly discuss a new approach to engaging DERs in providing transmission level services. The core theory focus of the talk will be the development and analysis of an approach to utilize extremum seeking control for decentralized coordination of DER in real time.
[INMA] 2018-03-13 (14:00) : Approximate Bayesian inference in Machine Learning: where stats meets optimisation
At Euler building (room A.002)
Speaker :
Thibaut Lienart (University of Oxford)
Abstract :
In this talk I will discuss some recent work on distributed approximate Bayesian inference for Machine Learning where one is interested not only in finding good parameters for a given model (e.g.: the weights of a Neural Network) but also to model the uncertainty around these parameters and thereby model the uncertainty in the predictions made with the model.
This is of tremendous importance in modern applications of ML such as self-driving cars, healthcare applications etc.
I will show how approximate Bayesian inference converts a hard integration problem into a simpler optimisation problem and discuss some approaches that have been used to tackle the optimisation.
Finally, I will give a critical view on the field, hopefully leading to a discussion.
[INMA] 2018-03-06 (14:00) : Electricity Demand Response in practice - why simplicity matters in a complex system
At Euler building (room A.002)
Speaker :
Latiers, Arnaud
Abstract :
Demand Response has become an essential part of the Belgian Electric system operations.
This tools allows consumers to participate in the balancing of the grid, or in the value definition in electricity markets (demand curve, elasticity).
It brings value at all stages in the energy system : adequacy, security, efficiency.
While the technical potential is enormous, market potential relatively lower.
One of the biggest barriers to accessing more flexibility in the current market is the fact that more complex models must be put in place to access the unexploited flexible capacity.
Furthermore, services themselves are becoming less user friendly, which makes contractualisation more difficult.
In this talk, we will highlight how simplicity at all stages is the key for success.
Quite amazingly, it starts often by simpler mathematical formulation of the problem...
[INMA] 2018-02-27 (16:30) : Increasing stability of training of Deep CNNs with Stochastic Gradient Descent method. Application to Image classification tasks
At CORE, b-135
Speaker :
Jenny Benois-Pineau (University of Bordeaux)
Abstract :
[Note the unusual time and location.]
See https://uclouvain.be/en/research-institutes/immaq/core/events/operations-research-seminar-jenny-benois-pineau.html
[INMA] 2018-02-20 (14:00) : Back to the roots: Multivariate polynomial optimization by numerical linear algebra
At Euler building (room A.002)
Speaker :
Bart De Moor (KU Leuven)
Abstract :
Finding some or all of the roots of a set of multivariate polynomials has numerous applications. This century old problem is at the foundation of algebraic geometry sometimes called the 'queen of mathematics'. In this discipline, typically, numerical computations are done symbolically, and only quite recently, it was realized that the multivariate polynomial rooting problem can be tackled using the machinery of numerical linear algebra, because of the fundamental insight that it is equivalent to an eigenvalue problem. In order to find some or all roots, we deploy tools like the QR- and singular value decomposition, and (possibly large scale) iterative eigenvalue solvers. We will discuss several eigenvalue decomposition based algorithms to calculate the global minimum of multivariate polynomial optimization problems. We will illustrate our approach with the numerical solution of two open problems in system theory: Calculating from (noisy) data linear dynamic models that are least squares optimal, and finding the least squares global optimum of the H2 model reduction problem.
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