All Years Seminars
[INMA] 2022-09-20 (14h) : The Polyak-Lojasiewicz condition and overparameterized learning
At Euler building (room a.002)
Speaker :
Dávid Terjék (Renyi Institute Budapest)
,
and
Diego González-Sánchez (Renyi Institute Budapest)
Abstract : We study the convergence of gradient descent on a composition (f ∘ F): G -> R of functions F: G -> H and f: H -> R where G, H are Hilbert spaces. We require f to satisfy the Lipschitz gradient and Polyak-Łojasiewicz conditions, and we find sufficient conditions on F that ensure convergence at a linear rate to a global optimum close to the initial point. We then propose a prototype optimization problem, covering a range of machine learning problems including supervised learning. Applying the results gives us sufficient conditions on the neural network and the data that ensure the global convergence of gradient descent, with the Neural Tangent Kernel making a natural appearance.
[INMA] 2022-04-26 (14h) : Automating data exploration
At Euler building (room a.002)
Speaker :
Tijl De Bie (UGent)
Abstract : Machine learning is increasingly becoming a commodity, available as a building block to programmers with minimal required understanding about detailed modelling aspects. In large part, this is thanks to advances in automating the labor-intensive aspects of machine learning. In contrast, the more exploratory tasks in data science still remain highly labor-intensive, often requiring a deeper understanding of the intricacies of the methods used. In this talk, I will survey some past results on the automation of data exploration tasks, and outline what I view as some of the more important challenges for the future.
[INMA] 2022-04-19 (14h) : A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
At Euler building (room a.002)
Speaker :
Raphaël Berthier (École Polytechnique Fédérale de Lausanne)
Abstract : We introduce the continuized Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuously mix following a linear ordinary differential equation and take gradient steps at random times. This continuized variant benefits from the best of the continuous and the discrete frameworks: as a continuous process, one can use differential calculus to analyze convergence and obtain analytical expressions for the parameters; and a discretization of the continuized process can be computed exactly with convergence rates similar to those of Nesterov original acceleration. We show that the discretization has the same structure as Nesterov acceleration, but with random parameters. We provide continuized Nesterov acceleration under deterministic as well as stochastic gradients, with either additive or multiplicative noise. Finally, using our continuized framework and expressing the gossip averaging problem as the stochastic minimization of a certain energy function, we provide the first rigorous acceleration of asynchronous gossip algorithms.
[INMA] 2022-04-12 (14h) : Data-driven Decision-Making in Dynamic Environments
At Euler building (room a.002)
Speaker :
Peyman Esfahani (Delft Centre for Systems and Control (TU Delft))
Abstract : In this seminar, we start with a broad class of anomaly detection for large-scale nonlinear dynamical systems. Noting a connection between the diagnosis filter and the so-called behavioral sets of dynamical systems, we leverage tools from the traditional model-based approaches and modern data-driven analytics to address the inherent complexity of the problem. We then shift our attention to the performance guarantees of our proposed solution. In this part, we study this topic in a general context of data-driven decision-making with a particular focus on the distributionally robust optimization framework. We will discuss the role of convexity from the different viewpoints of computational, statistical, and real-time implementation.
[INMA] 2022-03-29 (14h) : Reconstructing the climate of the past millennium by combining indirect climate observations and simulation results
At Euler building (room a.002)
Speaker :
Hugues Goosse (UCLouvain (ELI))
Abstract : It is essential to characterize well past natural climate variations to detect the potential anthropogenic contribution in recent changes. Past variations also allow studying the dynamics of the climate system for a range of conditions broader than the one observed recently. The past millennium appears particularly interesting in this framework as the conditions are similar to those observed currently, except that the human impact was much lower. The reconstruction of past climate changes is based on indirect observations as the instrumental data cover less than 150 years in most regions. Different techniques exists. The goal here is to illustrate how climate models results and indirect records derived from natural archives can be combined to obtain estimates of past states of the climate system and of the mechanisms responsible for the past climate changes.
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+32 10 47 80 36
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Mon – Fri 9:00A.M. – 5:00P.M.
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