Dear all,

The talk by Samory Kpotufe is tomorrow:

Our seminar speaker this Friday in the thematic seminar is Samory Kpotufe. Further below, there is also a list of upcoming talks that are scheduled for the second semester.

*Samory Kpotufe* (Department of Statistics, Columbia University, http://www.columbia.edu/~skk2175/) Samory works on the intersection between statistics and machine learning, with an interest in adaptive methods, and was one of the chairs for last year's COLT learning theory conference.

*Friday November 26*, 16.00-17.00 Online on Zoom: https://uva-live.zoom.us/j/85155421740 Meeting ID: 851 5542 1740

Please also join for online drinks after the talk.

*Some Recent Insights on Transfer and Multitask Learning*

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 mechanism or due to prohibitive target sampling costs. In such situations, we aim to ’transfer’ relevant information from the training data (a.k.a. source data) to the target application. How much information is in the source data about the target application? Would some amount of target data improve transfer? These are all practical questions that depend crucially on 'how far' the source domain is from the target. However, how to properly measure 'distance' between source and target domains remains largely unclear. In this talk we will argue that much of the traditional notions of 'distance' (e.g. KL-divergence, extensions of TV such as D_A discrepancy, density-ratios, Wasserstein distance) can yield an over-pessimistic picture of transferability. Instead, we show that some asymmetric notions of 'relatedness' between source and target (which we simply term 'transfer-exponents') capture a continuum from easy to hard transfer. Transfer-exponents uncover a rich set of situations where transfer is possible even at fast rates; they encode relative benefits of source and target samples, and have interesting implications for related problems such as 'multi-task or multi-source learning'. In particular, in the case of transfer from multiple sources, we will discuss (if time permits) a strange phenomena: no procedure can guarantee a rate better than that of having a single data source, even in seemingly mild situations where multiple sources are informative about the target. The talk is based on earlier work with Guillaume Martinet, and ongoing work with Steve Hanneke.

Seminar organizers: Tim van Erven Botond Szabo

https://mschauer.github.io/StructuresSeminar/

*Upcoming talks:*

Mar. 11, 2022, *Tomer Koren https://mschauer.github.io/StructuresSeminar/#Koren, *Tel Aviv University Mar. 25, 2022, *Nicolò Cesa-Bianchi https://mschauer.github.io/StructuresSeminar/#CesaBianchi**, *Università degli Studi di Milano Apr. 8, 2022, *Julia Olkhovskaya https://sites.google.com/view/julia-olkhovskaya/home*, Vrije Universiteit Apr. 22, 2022, *Tor Lattimore https://mschauer.github.io/StructuresSeminar/#Lattimore*, DeepMind