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Samory Kpotufe (Columbia University) – "Some Recent Insights on Transfer-Learning"

Statistical Seminar: Every Monday at 2:00 pm. Time: 3:00 pm - 4:15 pm Date: 7th of June 2021 Place: Visio Samory Kpotufe (Columbia University) - "Some Recent Insights on Transfer-Learning" Abstract: A common situation in Machine Learning is one where training data is not fully representative of a target population due to bias in the sampling […]

Jonathan NILES-WEED (NYU) – "The “all-or-nothing” phenomenon in sparse estimation"

Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 14th of June 2021 Place: Visio Jonathan NILES-WEED (NYU) - "The "all-or-nothing" phenomenon in sparse estimation" Abstract: We explore a sharp phase transition known as the "all-or-nothing" phenomenon in estimation problems. This phenomenon arises when there exists a critical signal to noise […]

Stanislav Minsker (University of South California) – “Towards robust and efficient mean estimation”

Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 6th of September 2021 Place: salle 1001 et en visio Stanislav Minsker (University of South California)  - "Towards robust and efficient mean estimation" Abstract: Several constructions of the estimators of the mean of a random variable that admit sub-Gaussian deviation guarantees and […]

Richard Gill (Leiden University) – “Bell experiments, Bell-denialism, and the quantum Randi challenge “

Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 4th of October 2021 Place: en visioRichard Gill (Leiden University) - "Bell experiments, Bell-denialism, and the quantum Randi challenge " Abstract: John S. Bell’s 1965 theorem, as a piece of pure mathematics, states that the predictions of quantum mechanics cannot be reproduced […]

Jonathan Scarlett (National University of Singapore) – “Recent Developments in High-Dimensional Estimation with Generative Priors”

Statistical Seminar: Every Monday at 2:00 pm. Time: 3:00 pm - 4:15 pm exceptionally Date: 11th of October 2021 Place: visio Jonathan Scarlett (National University of Singapore) - "Recent Developments in High-Dimensional Estimation with Generative Priors" Abstract: The problem of estimating an unknown vector (or image) from linear or non-linear measurements has a long history in […]

Lorenzo ROSASCO (Università di Genova & MIT) – “Interpolation and learning with scale dependent kernels “

Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 8th of November 2021 Place: visio Lorenzo ROSASCO (Università di Genova & MIT) - "Interpolation and learning with scale dependent kernels" Abstract: We study the learning properties of nonparametric ridge-less least squares. In particular, we consider the common case of estimators defined […]

Daniel HSU (Columbia University) – “Computational Lower Bounds for Tensor PCA”

Statistical Seminar: Every Monday at 2:00 pm. Time: 3:00 pm - 4:15 pm exceptionally Date: 15th of November 2021 Place: visio Daniel HSU (Columbia University) - "Computational Lower Bounds for Tensor PCA " Abstract: Tensor PCA is a model statistical inference problem introduced by Montanari and Richard in 2014 for studying method-of-moments approaches to parameter estimation […]

Julie JOSSE (INRIA) – “Causal effect on a target population: a sensitivity analysis to handle missing covariates “

Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 29th of November 2021 Place: visio Julie JOSSE (INRIA) - "Causal effect on a target population: a sensitivity analysis to handle missing covariates" Abstract: Randomized controlled trials (RCTs) are considered the gold standard approach for assessing the causal effect of an intervention […]

Yihong Wu (Yale University) – “Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models”

Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 31th of January 2022 Place: en visio Yihong Wu (Yale University) - "Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models" Abstract: Introduced by Kiefer and Wolfowitz 1956, the nonparametric maximum likelihood estimator (NPMLE) is a widely used methodology for learning […]