[theory-seminar] "Robust Learning from Batches -- The Best Things in Life are (Almost) Free" – Alon Orlitsky (Thu, 11-Mar @ 4:30pm)

[Joachim Neu]

Robust Learning from Batches -- The Best Things in Life are (Almost)
Free

                    Alon Orlitsky – Professor, UC San Diego

                    Thu, 11-Mar / 4:30pm
                        
                         / Zoom: 
https://stanford.zoom.us/meeting/register/tJckfuCurzkvEtKKOBvDCrPv3McapgP6HygJ
                        
                    

                    
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Abstract


In many applications, including natural language processing, sensor 
networks, collaborative filtering, and federated learning, data are 
collected in batches, some potentially corrupt, biased, or even 
adversarial. Learning algorithms for this setting have therefore 
garnered considerable recent attention. We develop a general framework 
for robust learning from batches, and determine the least number of 
samples required for robust density estimation and classification over 
both discrete and continuous domains. Perhaps surprisingly, we show
that
 robust learning can be achieved with essentially the same number of 
samples as required for genuine data. For the important problems of 
learning discrete and piecewise-polynomial densities, and of 
interval-based classification, we achieve these limits with 
polynomial-time algorithms.


Based on joint work with Ayush Jain.


Bio


Alon Orlitsky received B.Sc. degrees in Mathematics and Electrical 
Engineering from Ben Gurion University, and M.Sc. and Ph.D. degrees in 
Electrical Engineering from Stanford University. After a decade with
the
 Communications Analysis Research Department of Bell Laboratories and
a 
year as a quantitative analyst at D.E. Shaw and Company, he joined the 
University of California San Diego, where he is currently a professor
of
 Electrical and Computer Engineering and of Computer Science and 
Engineering and holds the Qualcomm Chair for Information Theory and
its 
Applications. His research concerns information theory, statistical 
modeling, and machine learning, focusing on fundamental limits and 
practical algorithms for extracting knowledge from data. Among other 
distinctions, Alon is a recipient of the 2021 Information Theory
Society
 Shannon Award and a co-recipient of the 2017 ICML Best Paper
Honorable 
Mention Award, the 2015 NeurIPS Paper Award, and the 2006 Information 
Theory Society Paper Award.



                    
                    
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