Elias Jääsaari



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ejaeaesa@andrew.cmu.edu

About

I am a first year PhD student in Machine Learning at Carnegie Mellon University, where I am advised by Tianqi Chen and affiliated with the Catalyst research group. I am interested in automated and scalable machine learning systems and algorithms.

Before starting my PhD, I lived in Cambridge, UK, where I was an early employee at a University of Cambridge spin-out company writing algorithms and building systems for large-scale semantic similarity search. I am originally from Finland and received my BSc and MSc degrees in Computer Science from the University of Helsinki, where I was advised by Teemu Roos and affiliated with the Information, Complexity and Learning research group.

Publications

E. Jääsaari, V. Hyvönen, T. Roos. Efficient autotuning of hyperparameters in approximate nearest neighbor search. Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019). [pdf]

T. Silander, J. Leppä-aho, E. Jääsaari, T. Roos. Quotient normalized maximum likelihood criterion for learning Bayesian network structures. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018). [pdf]

E. Jääsaari, J. Leppä-aho, T. Silander, T. Roos. Minimax optimal Bayes mixtures for memoryless sources over large alphabets. Proceedings of the 29th International Conference on Algorithmic Learning Theory (ALT 2018). [pdf]

V. Hyvönen, T. Pitkänen, S. Tasoulis, E. Jääsaari, R. Tuomainen, L. Wang, J. Corander, T. Roos. Fast nearest neighbor search through sparse random projections and voting. Proceedings of the 4th IEEE International Conference on Big Data (IEEE Big Data 2016). [pdf] [code]