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Machine Learning Theory
We aim to advance the theoretical understanding of learning through the analysis of new
induction principles and through a paradigm shift from traditional inductive inference
to non-inductive inference such as transductive and selective inference.
Fast Learning
Accelerating computation
by orders of magnitude through the development
of more efficient learning algorithms, and through more efficient implementations.
Algorithms & Applications Applications of our theoretical and algorithmic advances to innovative applications, showcasing their performance. (See all projects.) |
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