Algorithms with Machine Learned Advice
Two years ago a new research area that uses machine learning to improve classic algorithms and data structures was born. Since then, applications of this approach to indexing, hashing, text indexing, on-line and streaming algorithms have been presented and further ones are being developed. Classic algorithms are designed to protect against worst-case scenarios, but they tend to be overly cautious on average. On the other hand, machine learning is good only on average and is best at learning just a few things at a time. Learning augmented algorithms take the best out of the two worlds. During the talk I will provide an introduction to this area and demonstrate main ideas behind selected results.
Jakub Radoszewski works as a Principal Data Scientist at Samsung R&D, Poland and as an assistant professor at the University of Warsaw, Poland. He is also a Vice-Chair of the Main Committee of the Polish Olympiad in Informatics. Jakub Radoszewski received his PhD from the University of Warsaw in 2012. Later he spent 2 years as a Newton International Fellow at King’s College London, UK. He received the Witold Lipski Award for young researchers in computer science, best paper awards at several conferences, and has lead a few research projects on theoretical computer science. His main research area are discrete algorithms, with a focus on text algorithms. He is also interested in the interplay between algorithms and ML.