Atelier de problèmes inverses et apprentissage machine27 au 29 mai 2019Programme |
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Le lundi 27 mai 2019 |
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08:30 - 09:00
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Inscription (salle 5345) et café-croissants (salle 6245)
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09:00 - 09:40
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Mikhail (Misha) Belkin
(The Ohio State University) Rethinking the bias-variance trade-off | Résumé |
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09:45 - 10:25
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Dirk Lorenz
(Technische Universität Braunschweig) Unrolling of primal-dual algorithms | Résumé |
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10:30 - 11:10
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Pause-café
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11:10 - 11:50
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Dejan Slepcev
(Carnegie Mellon University) Proper weighted Laplacian for semi-supervised learning | Résumé |
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12:00 - 13:30
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Pause-déjeuner
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13:30 - 14:10
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Michael Mahoney
(UC Berkeley) Why deep learning works: traditional and heavy-tailed implicit self-regularization in deep neural networks | Résumé |
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14:15 - 14:55
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Christoph Brune
(University of Twente) Deep learning decomposition for inverse problems | Résumé |
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15:00 - 15:30
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Pause-café
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15:30 - 16:10
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Venkat Chandrasekaran
(California Institute of Technology) A geometric perspective on false discovery control | Résumé |
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16:15 - 16:55
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Giovanni S. Alberti
(Universit`a di Genova) Adversarial deformations in deep neural networks | Résumé |
Le mardi 28 mai 2019 |
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08:30 - 09:00
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Café croissants
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09:00 - 09:40
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Jeffrey Willam Calder
(University of Minnesota) PDE continuum limits for prediction with expert advice | Résumé |
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09:45 - 10:25
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Christoph Schwab
(ETH Zurich) Deep neural network expression in Bayesian PDE constrained data assimilation and inversion | Résumé |
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10:30 - 11:10
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Pause-café
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11:10 - 11:50
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Bamdad Hosseini
(California Institute of Technology) Consistency of semi-supervised learning algorithms on graphs | Résumé |
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12:00 - 13:30
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Pause-déjeuner
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13:30 - 14:10
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Nicolas Le Roux
(Google Brain - Montréal) On the interplay between noise and curvature in deep learning | Résumé |
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14:15 - 14:55
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Matteo Santacesaria
(University of Genoa) Inverse problems for PDEs via compressed sensing | Résumé |
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15:00 - 15:30
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Pause-café
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15:30 - 16:10
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Matthew Thorpe
(University of Cambridge) On the well posedness of graph Laplacian regularisation in semi-supervised learning | Résumé |
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16:15 - 16:55
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Nikola Kovachki
(California Institute of Technology) Continuous time limits for momentum methods as implemented in machine learning | Résumé |
Le mercredi 29 mai 2019 |
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08:30 - 09:00
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Café croissants
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09:00 - 09:40
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Mauro Maggioni
(Johns Hopkins University) Statistical learning & dynamical systems: exploiting hidden low-dimensional structures | Résumé |
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09:45 - 10:25
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Bharath Sriperumbudur
(The Pennsylvania State University) Distribution regression: theory and applications |
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10:30 - 11:10
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Pause-café
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11:10 - 11:50
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Daniel Sanz-Alonso
(University of Chicago) Function-space inverse problems and semi-supervised learning | Résumé |
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12:00 - 13:30
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Pause-déjeuner
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13:30 - 14:10
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Franca Hoffmann
(California Institute of Technology) Geometric insights into spectral clustering by graph Laplacian embeddings | Résumé |
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14:15 - 14:55
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Eldad Haber
(The University of British Columbia) Efficient architectures for deep neural networks | Résumé |
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15:00 - 15:30
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Pause-café
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15:30 - 16:10
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Alfredo Garbuno Inigo
(Caltech) Optimize, learn, sample | Résumé |
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16:15 - 16:55
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Adam M. Oberman
(McGill University) |
Mise à jour: Le lundi 27 mai 2019 14:07