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IDSS Distinguished Seminars Lisa Goldberg, UC Berkeley

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Stochastics and Statistics Seminar Florian Gunsilius, University of Michigan

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IDSS Celebration

IDSS Celebration

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Stochastics and Statistics Seminar Matias Cattaneo, Princeton University

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Stochastics and Statistics Seminar Samory Kpotufe, Columbia University

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Stochastics and Statistics Seminar Vianney Perchet, ENSAE Paris

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IDSS Distinguished Seminars

James-Stein for eigenvectors: reducing the optimization bias in Markowitz portfolios

Lisa Goldberg, UC Berkeley

Abstract: We identify and reduce bias in the leading sample eigenvector of a high-dimensional covariance matrix of correlated variables. Our analysis illuminates how error in an estimated covariance matrix corrupts optimization. It may be applicable in finance, machine learning and genomics. Biography: Lisa Goldberg is Head of Research at Aperio and Managing Director at BlackRock.…

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Stochastics and Statistics Seminar

Free Discontinuity Design (joint w/ David van Dijcke)

Florian Gunsilius, University of Michigan
E18-304

Abstract: Regression discontinuity design (RDD) is a quasi-experimental impact evaluation method ubiquitous in the social- and applied health sciences. It aims to estimate average treatment effects of policy interventions by exploiting jumps in outcomes induced by cut-off assignment rules. Here, we establish a correspondence between the RDD setting and free discontinuity problems, in particular the…

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IDSS Celebration

This celebratory event reflects on the impact in research and education the Institute for Data, Systems, and Society has had since its launch in 2015 and explores future opportunities with thought leaders and policy experts. In panels and plenary talks, we will discuss the impact of research areas utilizing the available massive data, in-depth understanding…

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IDSS Celebration

This celebratory event reflects on the impact in research and education the Institute for Data, Systems, and Society has had since its launch in 2015 and explores future opportunities with thought leaders and policy experts. In panels and plenary talks, we will discuss the impact of research areas utilizing the available massive data, in-depth understanding…

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Stochastics and Statistics Seminar

Adaptive Decision Tree Methods

Matias Cattaneo, Princeton University
E18-304

Abstract: This talk is based on two recent papers: 1. “On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation” and 2. “Convergence Rates of Oblique Regression Trees for Flexible Function Libraries” 1. Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects…

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Stochastics and Statistics Seminar

Adaptivity in Domain Adaptation and Friends

Samory Kpotufe, Columbia University
E18-304

Abstract: Domain adaptation, transfer, multitask, meta, few-shots, representation, or lifelong learning … these are all important recent directions in ML that all touch at the core of what we might mean by ‘AI’. As these directions all concern learning in heterogeneous and ever-changing environments, they all share a central question: what information a data distribution…

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Stochastics and Statistics Seminar

Learning learning-augmented algorithms. The example of stochastic scheduling

Vianney Perchet, ENSAE Paris
E18-304

Abstract: In this talk, I will argue that it is sometimes possible to learn, with techniques originated from bandits, the "hints" on which learning-augmented algorithms rely to improve worst-case performances. We will describe this phenomenon, the combination of online learning with competitive analysis, on the example of stochastic online scheduling. We shall quantify the merits…

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Massachusetts Institute of Technology
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