As Gabriel mentioned on his twitter feed, several of the past seminars of the Data Science Colloquium of the ENS are here:
March. 7th, 2017, Francis Bach (INRIA) [video]
Title: Beyond stochastic gradient descent for large-scale machine learning
Abstract: Many machine learning and signal processing problems are traditionally cast as convex optimization problems. A common difficulty in solving these problems is the size of the data, where there are many observations ('large n') and each of these is large ('large p'). In this setting, online algorithms such as stochastic gradient descent which pass over the data only once, are usually preferred over batch algorithms, which require multiple passes over the data. In this talk, I will show how the smoothness of loss functions may be used to design novel algorithms with improved behavior, both in theory and practice: in the ideal infinite-data setting, an efficient novel Newton-based stochastic approximation algorithm leads to a convergence rate of O(1/n) without strong convexity assumptions, while in the practical finite-data setting, an appropriate combination of batch and online algorithms leads to unexpected behaviors, such as a linear convergence rate for strongly convex problems, with an iteration cost similar to stochastic gradient descent. (joint work with Nicolas Le Roux, Eric Moulines and Mark Schmidt).
Jan. 10th, 2017, Bertrand Thirion (INRIA and Neurospin) [video]
Title: A big data approach towards functional brain mapping
Abstract: Functional neuroimaging offers a unique view on brain functional organization, which is broadly characterized by two features: the segregation of brain territories into functionally specialized regions, and the integration of these regions into networks of coherent activity. Functional Magnetic Resonance Imaging yields a spatially resolved, yet noisy view of this organization. It also yields useful measurements of brain integrity to compare populations and characterize brain diseases. To extract information from these data, a popular strategy is to rely on supervised classification settings, where signal patterns are used to predict the experimental task performed by the subject during a given experiment, which is a proxy for the cognitive or mental state of this subject. In this talk we will describe how the reliance on large data copora changes the picture: it boosts the generalizability of the results and provides meaningful priors to analyze novel datasets. We will discuss the challenges posed by these analytic approaches, with an emphasis on computational aspects, and how the use of non-labelled data can be further used to improve the model learned from brain activity data.
Nov. 8th, 2016, Cristopher Moore (Santa Fe Institute) [video]
Title: What physics can tell us about inference?
Abstract: There is a deep analogy between statistical inference and statistical physics; I will give a friendly introduction to both of these fields. I will then discuss phase transitions in two problems of interest to a broad range of data sciences: community detection in social and biological networks, and clustering of sparse high-dimensional data. In both cases, if our data becomes too sparse or too noisy, it suddenly becomes impossible to find the underlying pattern, or even tell if there is one. Physics both helps us locate these phase transiitons, and design optimal algorithms that succeed all the way up to this point. Along the way, I will visit ideas from computational complexity, random graphs, random matrices, and spin glass theory.
Oct. 11th, 2016, Jean-Philippe Vert (Mines ParisTech, Institut Curie and ENS) [video]
Title: Can Big Data cure Cancer?
Abstract: As the cost and throughput of genomic technologies reach a point where DNA sequencing is close to becoming a routine exam at the clinics, there is a lot of hope that treatments of diseases like cancer can dramatically improve by a digital revolution in medicine, where smart algorithms analyze « big medical data » to help doctors take the best decisions for each patient or to suggest new directions for drug development. While artificial intelligence and machine learning-based algorithms have indeed had a great impact on many data-rich fields, their application on genomic data raises numerous computational and mathematical challenges that I will illustrate on a few examples of patient stratification or drug response prediction from genomic data.
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