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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK
Vitaly Kuznetsov

Vitaly Kuznetsov

Vitaly Kuznetsov is a research scientist at Google, where he focuses on the design and implementation of machine learning tools and algorithms for time series modeling, forecasting, and anomaly detection for a variety of practical applications ranging from supply forecasting for search ads to demand estimation in networks. Vitaly has contributed to a number of different areas in machine learning, including structured prediction, ensemble learning, deep learning, and the development of the theory and algorithms for forecasting nonstationary time series. He holds a PhD in mathematics from the Courant Institute of Mathematical Sciences at New York University.


11:05–11:45 Thursday, 11 October 2018
Models and Methods
Location: King's Suite - Balmoral
Secondary topics:  Deep Learning models, Temporal data and time-series
Vitaly Kuznetsov (Google), Zelda Mariet (MIT)
Vitaly Kuznetsov and Zelda Mariet compare sequence-to-sequence modeling to classical time series models and provide the first theoretical analysis of a framework that uses sequence-to-sequence models for time series forecasting. Read more.