Speaker |
Vladimir Vovk |
---|---|
Affiliation |
Royal Holloway University of London |
Date |
Friday, 23 January 2015 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 (Sir David Davies lecture theatre) |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
The topic of this talk will be probabilistic prediction using standard machine learning algorithms. The advantage of probabilities over alternative methods of quantifying uncertainty (such as prediction sets) is that they can be easily combined with losses and utilities for the purpose of decision making. For simplicity I will concentrate on problems of classification with two labels, 0 or 1. Most of machine learning algorithms are scoring algorithms in that they output not only a prediction but also a score intuitively reflecting the algorithm's confidence that the label is 1. One way of obtaining probabilistic predictions is to calibrate the scores. I will briefly review traditional calibration methods and describe a new method which is both computationally efficient and guaranteed to produce well-calibrated predictions. About the speaker: Vladimir Vovk graduated from Moscow State University, where he specialized in mathematical logic and did PhD in algorithmic randomness and Kolmogorov complexity. Since 1999 he is Professor of Computer Science at Royal Holloway, University of London. His research interests include machine learning and the foundations of probability. |
Biography |