All Years Seminars
[INMA] 2018-06-07 (14:00) : Distributed Optimization: A Tutorial
At Euler building (room A.002)
Speaker :
Olshevsky, Alex
Abstract :
This talk will provide a gentle introduction to the field of distributed optimization. The core question is how a collection of nodes interconnected in a network can cooperate to compute a minimizer of a convex function, provided each of them knows only a piece of this function. We will sketch the proof of a few of the results that have been obtained over the past decade and conclude with a list of open questions
[INMA] 2018-05-29 (14:00) : Leveraging historical data for high-dimensional regression adjustment, a machine learning approach
At Euler building (room A.002)
Speaker :
Samuel Branders (Tools4Patient)
Abstract :
The amount of data collected from patients involved in clinical trials is continuously growing. All those patient's characteristics are potential covariates that could be used to improve study analysis and power. At the same time, the development of computerized systems simplifies the access to huge amount of historical data. However, it is still difficult to leverage those data when dealing with small clinical trials, such as in Phases I and II. Their restricted number of patients limits the possible number of covariates included in the analysis.
The purpose of this talk is to present how machine learning can overcome this problem by taking advantage of historical data with larger sample sizes. Our approach is to pre-specify the combination of the baseline covariates by building a "meta-covariate". In small studies, using this meta-covariate alone will limit the loss of degrees of freedom while making the best uses of all generated data.
Two advantages of fitting the covariates on independent data are to free the modeling from the study constraints and to limit the risk of overfitting. Those are of particular interest with complex data, i.e non-normal distribution or in the presence of non-linearities.
To demonstrate the benefit of the methodology, we discuss several questions:
- What are too many covariates? Can we go beyond the simple rule-of-thumb 1 variable for 10 patients?
- What should be the minimum performance of the ML model in this context?
- When should we use machine learning?
[INMA] 2018-05-08 (14:00) : Data science for precision medicine
At Euler building (room A.002)
Speaker :
Thibault Helleputte (DNAlytics)
Abstract :
The field of precision medicine aims at developing new decision-support tools for the healthcare professionals, in order to improve patients health, healthcare system sustainability, and potentially better position some pharmaceutical products. These tools can be developed by combining a broad range of health-related data and mathematical modeling strategies, namely machine learning approaches. In this talk, we will discuss how some very well defined machine learning approaches for predictive model training, feature selection and performance evaluation survive the reality of biology, medicine and clinical applications.
[INMA] 2018-04-24 (14:00) : Locating the source of diffusion in large-scale and random networks
At Maxwell building, Shannon room
Speaker :
Thiran, Patrick
Abstract :
We will survey some results on the localization of the source of diffusion in a network. There have been significant efforts in studying the dynamics of epidemic propagations on networks, and more particularly on the forward problem of epidemics: understanding the diffusion process and its dependence on the infecting and curing rates. We address here the inverse problem of inferring the original source of diffusion, given the infection data gathered at some of the nodes in the network. Indeed, because of the large size of many real networks, the state of all nodes in a network cannot in general be observed. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization, and describe efficient implementations for arbitrary graphs. When propagation times are (close to) deterministic values, the smallest number of sensors needed to exactly localize the source is the double metric dimension of the network. We compute tight bounds for this quantity in a number of topologies that include Erdös-Renyi random graphs, and we comment on its implications for the detectability of a source in actual networks.
This is a joint work with Elisa Celis, Pedro Pinto, Brunella Spinelli and Martin Vetterli.
[INMA] 2018-04-17 (14:00) : The Hierarchical Adaptive Forgetting Variational Filter
At Euler building (room A.002)
Speaker :
Vincent Moens (COSY, Institute of Neuroscience, UCL)
Abstract :
A common problem in Machine Learning and statistics consists in detecting whether the current sample in a stream of data belongs to the same distribution as previous ones, is an isolated outlier or inaugurates a new distribution of data. We present a hierarchical Bayesian algorithm that aims at learning a time-specific approximate posterior distribution of the parameters describing the distribution of the data observed. We derive the update equations of the variational parameters of the approximate posterior at each time step for models from the exponential family, and show that these updates find interesting correspondents in Reinforcement Learning (RL). In this perspective, our model can be seen as a hierarchical RL algorithm that learns a posterior distribution according to a certain stability confidence that is, in turn, learned according to its own stability confidence. Finally, we show some applications of our generic model, first in a RL context, next with an adaptive Bayesian Autoregressive model, and finally in the context of Stochastic Gradient Descent optimization.
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