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Overview

The Aarhus Summer School on Learning Theory brings together top international PhD students to educate them on fundamental topics in theory of machine learning. The summer school takes place in beautiful Aarhus, Denmark. Aarhus is often mentioned as one of the happiest cities in the world and a hidden gem for travelers. This makes for a relaxing and inspiring environment for excursions, discussions, and collaborations.

Speakers

Shai Ben-David

Amin Karbasi

Amir Yehudayoff

Nikita Zhivotovskiy

Shai Ben-David

Prof Ben-David research interests span a range of topics in computer science theory.
He has been working on a wide range to topic including logic, theory of distributed computation and computational complexity. In recent years his focus turned to machine learning theory. Among his notable contribution in that field are pioneering steps in the analysis of domain adaptation, of learnability of real valued functions, and of change detection in streaming data. In the domain of unsupervised learning Shai has made fundamental contributions to the theory of clustering (developing tools for guiding users in picking algorithms to match their domain needs) and distribution learning. He has also published seminal works on average case complexity, competitive analysis and alternatives to worst-case complexity.
Prof Ben-David’s papers have won various awards, most recently, Best Paper awards in NeurIPS 2018 and in ALT 2023.

Shai earned his PhD in mathematics from the Hebrew University in Jerusalem and has been a professor of computer science at the Technion (Israel Institute of Technology).  Over the years, he has held visiting faculty positions at the Australian National University, Cornell University, ETH Zurich, TTI Chicago and the Simons institute at Berkeley.
Since 2004 Shai is a professor at the David Cheriton school of computer science at U Waterloo, and since 2019 a faculty member at the Vector Institute, Toronto. He is also a Canada CIFAR AI chair, a University research chair at U waterloo and a Fellow of the ACM. 

Highlights:
President of the Association for Computational Learning Theory (2009-2012).
Program chair for the major machine learning theory conferences (COLT and ALT, and area chair for ICML, NIPS and AISTATS). 
Co-authored the textbook "Understanding machine learning: from theory to algorithms".


Amin Karbasi
Amin Karbasi is currently an associate professor of Electrical Engineering, Computer Science, and Statistics & Data Science at Yale University. He is also a research staff scientist at Google NY. He has been the recipient of the National Science Foundation (NSF) Career Award, Office of Naval Research (ONR) Young Investigator Award, Air Force Office of Scientific Research (AFOSR) Young Investigator Award, DARPA Young Faculty Award, National Academy of Engineering Grainger Award, Bell Lab Prize, Amazon Research Award, Google Faculty Research Award, Microsoft Azure Research Award, Simons Research Fellowship, and ETH Research Fellowship. His work has also been recognized with a number of paper awards, including Medical Image Computing and Computer Assisted Interventions Conference (MICCAI) 2017, Facebook MAIN Award from Montreal Artificial Intelligence and Neuroscience Conference 2018, International Conference on Artificial Intelligence and Statistics (AISTAT) 2015, IEEE ComSoc Data Storage 2013, International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2011, ACM SIGMETRICS 2010, and IEEE International Symposium on Information Theory (ISIT) 2010 (runner-up). His Ph.D. thesis received the Patrick Denantes Memorial Prize 2013 from the School of Computer and Communication Sciences at EPFL, Switzerland.


Amir Yehudayoff
Amir received his Ph.D. from the Weizmann Institute of Science and was a two-year member at the Institute for Advanced Study in Princeton. He is currently a professor in the Department of Computer Science in the University of Copenhagen, and in the Department of Mathematics at the Technion. His main research area is theoretical computer science, with a recent focus on the theory of machine learning. 


Nikita Zhivotovskiy
Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California Berkeley. He previously held postdoctoral positions at ETH Zürich in the department of mathematics hosted by Afonso Bandeira, and at Google Research, Zürich hosted by Olivier Bousquet. He also spent time at the Technion I.I.T. mathematics department hosted by Shahar Mendelson. Nikita completed his thesis at Moscow Institute of Physics and Technology under the guidance of Vladimir Spokoiny and Konstantin Vorontsov.

Courses

TBA

Program

TBA

How To Apply

Please use the button below and fill out the form. The application deadline is May 1. Acceptance/rejection notifications and payment links will be sent within two weeks of the deadline. Attendance of the summer school, including lunch, coffee, cake, and one dinner, costs EUR 100 to be paid by May 21 to secure the spot. Accommodations and travel are not included.

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