All Years Seminars
[INMA] 2022-02-15 (14h) : Let's play! - Solving Controller Synthesis Games for Cyber-Physical System Design
At Euler building (room a.207)
Speaker :
Anne-Katrin Schmuck (Max Plank Institute for Software Systems, Kaiserslautern, Germany)
Abstract : Cyber-Physical Systems (CPS) are technical systems where a large software stack orchestrates the interaction of physical and digital components. Such systems are omnipresent in our daily life and their correct behavior is crucial. However, developing safe, reliable and performant CPS is challenging.
A promising research direction towards this goal is the combination of formal methods from computer science and controller synthesis techniques from automation. In my talk, I will discuss how infinite two-player games over finite graphs, originating from the formal methods community, can be utilized and enhanced for higher layer control of CPS. In particular, I will discuss how the use of environment assumptions - used to model particularities of the system under control within these games - has to be rethought in order to effectively solve controller synthesis tasks for CPS.
[INMA] 2022-02-08 (14h) : Welcome Seminar
At Euler building (room a.002)
Section 1:Extension of the Performance Estimation framework through constraint interpolation.
Speaker :
Anne Rubbens (PhD UCLouvain/INMA)
Abstract : Selecting parameters involved in an optimization algorithm is a major challenge often without satisfactory solution, and can have a dramatic impact on the algorithm performance. For instance, selecting parameters by optimizing performance bounds that are not exact may lead to misguided choices.Recently, an approach called Performance Estimation Problem (PEP) has been developed to automatically compute exact worst-case bounds on the performance of a wide class of optimization algorithms whose input belongs to a given class of functions, for instance convex functions.This project consists in extending the field of application of this approach to classes of functions for which no satisfactory PEP formulation exists yet. PEP formulations rely on necessary and sufficient interpolation conditions, ensuring the existence of a function of a given class interpolating a finite set of data. Hence, we will provide a novel approach to derive interpolation conditions for various classes of functions.
Section 2:Optimization of compositional functions
Speaker :
Bousselmi, Nizar
Abstract : We consider functions with compositional structure, i.e., functions that can be expressed using one or several compositions of simple elementary component functions. These functions naturally appear in many practical problems. We would like to analyze the impact of the compositional structure of objective functions on the performance of the existing methods and to develop new methods exploiting this structure.
Section 3:Automatic quality control of weather and climate time series
Speaker :
Loucheur, Benoît
Abstract : In Belgium, the Royal Meteorological Institute (RMI) is the national meteorological service that provide weather and climate services based on observations and scientific research. The RMI collects and archives meteorological observations in Belgium since the 19th century. Currently, air temperature is monitored in Belgium in about 30 synoptic automatic weather stations as well as in 110 manual climatological stations. All observations are routinely checked for errors, inconsistencies and missing values by the RMI staff. Misleading data are corrected and gaps are filled by estimations. This quality control tasks require a lot of human intervention. With the forthcoming deployment of low-cost weather stations and the subsequent increase in the volume of data to verify, the process of data quality control and completion should become as automated as much as possible. The aim of this project is to develop algorithmic tools to address this quality control problem in a fully automatic way.
[INMA] 2021-12-14 (14h) : Geomstats: a Python Package for Riemannian Geometry in Statistics and Machine Learning
At Euler building (room a.002)
Speaker :
Miolane, Nina
Abstract : We introduce Geomstats, an open-source Python package for computations and statistics on nonlinear manifolds that appear in machine learning applications, such as: hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Manifolds come equipped with families of Riemannian metrics with associated exponential and logarithmic maps, geodesics, and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering, and dimension reduction on manifolds. All associated operations provide support for different execution backends --- namely NumPy, Autograd, PyTorch, and TensorFlow. This talk presents the package, compares it with related libraries, and provides relevant examples. We show that Geomstats provides reliable building blocks to both foster research in differential geometry and statistics and democratize the use of Riemannian geometry in statistics and machine learning. The source code is freely available under the MIT license at https://github.com/geomstats/geomstats.
[INMA] 2021-12-07 (14h) : The symbiotic relationship between optimization and deep learning
At Euler building (room a.002)
Speaker :
Ablin, Pierre
Abstract : Optimization is one of the cornerstones of deep learning: most deep neural networks are trained by optimizing a cost function. The purpose of this talk is to cover other fruitful interactions between the fields of optimization and deep learning that are less obvious. First, I will discuss how deep neural networks can be designed to quickly and efficiently solve classical optimization problems, with a focus on the Lasso. I will then argue that the tools of optimization allow us to describe accurately what weights are learned by these networks during the training process. In a second part, I will discuss how classical optimization ideas like momentum acceleration can be translated to deep learning, and allow us to develop novel architectures that perform well and are memory-efficient.
[INMA] 2021-11-30 (14h) : A Generalized Worst-Case Complexity Analysis for Non-Monotone Line Searches
At Euler building (room a.002)
Speaker :
Nunes Grapiglia, Geovani
Abstract : In this talk we discuss the worst-case complexity of a wide class of non-monotone line search methods for non-convex unconstrained minimization problems. For the algorithms in this class, the non monotonicity is controlled by a sequence of nonnegative parameters. We prove complexity bounds to achieve approximate first-order optimality even when this sequence is not summable. As a by-product, we obtain a unified global convergence result. Our generalized results allow more freedom for the development of new non-monotone line search algorithms. As an example, we design a non-monotone scheme related to the Metropolis rule. Preliminary numerical experiments suggest that the new method is suitable to nonconvex problems with many non-global local minimizers.
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