Colloquia: Difference between revisions

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-<b>UW Madison mathematics Colloquium is ONLINE on Fridays at 4:00 pm. </b>
+<b>UW-Madison Mathematics Colloquium is on Fridays at 4:00 pm in Van Vleck B239 unless otherwise noted.</b>
-<!--- in Van Vleck B239, '''unless otherwise indicated'''. --->
+Contacts for the colloquium are Michael Kemeny (spring) and Dallas Albritton (fall).
-=Spring 2021=
+Everyone in the math department is subscribed to the mathcolloquium@g-groups.wisc.edu mailing list.
-== January 27, 2021 '''[Wed 4-5pm]''', [https://sites.google.com/view/morganeaustern/home Morgane Austern] (Microsoft Research) ==
-(Hosted by Roch)
+This semester's colloquia: [[Colloquia/Spring 2025|Spring 2025]]
+==Future Colloquia==
+==Past Colloquia ==
-'''Asymptotics of learning on dependent and structured random objects'''
+[[Colloquia/Fall 2024|Fall 2024]]
-Classical statistical inference relies on numerous tools from probability theory to study
+[[Colloquia/Spring2024|Spring 2024]]
-the properties of estimators. However, these same tools are often inadequate to study
-modern machine problems that frequently involve structured data (e.g networks) or
-complicated dependence structures (e.g dependent random matrices). In this talk, we
-extend universal limit theorems beyond the classical setting.
-Firstly, we consider distributionally “structured” and dependent random object–i.e
+[[Colloquia/Fall 2023|Fall 2023]]
-random objects whose distribution are invariant under the action of an amenable group.
-We show, under mild moment and mixing conditions, a series of universal second and
-third order limit theorems: central-limit theorems, concentration inequalities, Wigner
-semi-circular law and Berry-Esseen bounds. The utility of these will be illustrated by
-a series of examples in machine learning, network and information theory. Secondly
-by building on these results, we establish the asymptotic distribution of the cross-
-validated risk with the number of folds allowed to grow at an arbitrary rate. Using
-this, we study the statistical speed-up of cross validation compared to a train-test split
-procedure, which reveals surprising results even when used on simple estimators.
-== January 29, 2021, [https://sites.google.com/site/isaacpurduemath/ Isaac Harris] (Purdue) ==
+[[Colloquia/Spring2023|Spring 2023]]
-(Hosted by Smith)
+[[Colloquia/Fall2022|Fall 2022]]
-== February 1, 2021 '''[Mon 4-5pm]''', [https://services.math.duke.edu/~nwu/index.htm Nan Wu] (Duke) ==
+[[Spring 2022 Colloquiums|Spring 2022]]
-(Hosted by Roch)
+[[Colloquia/Fall2021|Fall 2021]]
-'''From Manifold Learning to Gaussian Process Regression on Manifolds'''
+[[Colloquia/Spring2021|Spring 2021]]
-In this talk, I will review the concepts in manifold learning and discuss a famous manifold learning algorithm, the Diffusion Map. I will talk about my recent research results which theoretically justify that the Diffusion Map reveals the underlying topological structure of the dataset sampled from a manifold in a high dimensional space. Moreover, I will show the application of these theoretical results in solving the regression problems on manifolds and ecological problems in real life.
-== February 5, 2021, [https://hanbaeklyu.com/ Hanbaek Lyu] (UCLA) ==
-(Hosted by Roch)
-== February 8, 2021 '''[Mon 4-5pm]''', [https://sites.google.com/view/mndaoud/home Mohamed Ndaoud] (USC) ==
-(Hosted by Roch)
-== February 12, 2021, [https://sites.math.washington.edu/~blwilson/ Bobby Wilson] (University of Washington) ==
-(Hosted by Smith)
-== February 19, 2021, [http://www.mauricefabien.com/ Maurice Fabien] (Brown)==
-(Hosted by Smith)
-== February 26, 2021, [https://www.math.ias.edu/avi/home Avi Wigderson] (Princeton IAS) ==
-(Hosted by Gurevitch)
-== March 12, 2021, [] ==
-(Hosted by )
-== March 26, 2021, [] ==
-(Hosted by )
-== April 9, 2021, [] ==
-(Hosted by )
-== April 23, 2021, [] ==
-(Hosted by )
-== Past Colloquia ==
 [[Colloquia/Fall2020|Fall 2020]]

Colloquia: Difference between revisions

Latest revision as of 06:22, 17 December 2024

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