Boris Bukh (Carnegie Mellon University)
Discrete Geometry
10:15 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room G 43
François Bachoc (University of Toulouse)
Title T.B.A.
11:00 • EPF Lausanne, Extranef 125
Dr. Seoyoung Kim (UBasel)
Title tba: Seoyoung Kim
14:15 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room G 43
Daniel Nevo (Tel Aviv University)
Title T.B.A.
15:15 • EPF Lausanne, CM 1 517
Dr. Michele Graffeo (SISSA)
The motive of the Hilbert scheme of points abstract
Abstract:
The Hilbert scheme of points on a quasi-projective variety is a classical object in algebraic geometry. However, its geometry is nowadays still not completely accessible. On the other hand, the motive of a variety X is an invariant attached to X carrying a lot of information about its geometry, and it is considered as a universal Euler characteristic. In a joint project with Monavari, Moschetti and Ricolfi we give general formulas to compute the motive of the Hilbert scheme of points, provided the knowledge of a finite amount of data (that we gave explicitly in some cases). In my seminar I will present our formulas and I will show many applications.
16:00 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room G 43
David M. Blei (Columbia University)
Joint talk ETH-FDS Seminar - Research Seminar on Statistics: "Hierarchical Causal Models" abstract
Abstract:
Analyzing nested data with hierarchical models is a staple of Bayesian statistics, but causal modeling remains largely focused on "flat" models. In this talk, we will explore how to think about nested data in causal models, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data---replacing a flat causal model with a hierarchical causal model---can provide new opportunities foridentification and estimation. As examples, we will study how toidentify and estimate causal effects under unmeasured confounders, interference, and instruments.Preprint: https://arxiv.org/abs/2401.05330This is joint work with Eli Weinstein.
16:15 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room D 7.2
David M. Blei (Columbia University)
Joint talk ETH-FDS Seminar - Research Seminar on Statistics:"Hierarchical Causal Models" abstract
Abstract:
Analyzing nested data with hierarchical models is a staple of Bayesian statistics, but causal modeling remains largely focused on "flat" models. In this talk, we will explore how to think about nested data in causal models, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data---replacing a flat causal model with a hierarchical causal model---can provide new opportunities foridentification and estimation. As examples, we will study how toidentify and estimate causal effects under unmeasured confounders, interference, and instruments.Preprint: https://arxiv.org/abs/2401.05330This is joint work with Eli Weinstein.
16:15 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room D 7.2