200 citations · 308 across the 11 of their papers we have counts for
8 papers · 1 filter
Diverse mini-batch Active Learning
Fedor Zhdanov
We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through seque…
An Identity for Kernel Ridge Regression
Fedor Zhdanov, Yuri Kalnishkan
This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the proper…
Prediction with Expert Advice under Discounted Loss
Alexey Chernov, Fedor Zhdanov
We study prediction with expert advice in the setting where the losses are accumulated with some discounting---the impact of old losses may gradually vanish. We generalize the Aggr…
Supermartingales in Prediction with Expert Advice
Alexey Chernov, Yuri Kalnishkan, Fedor Zhdanov +1
We apply the method of defensive forecasting, based on the use of game-theoretic supermartingales, to prediction with expert advice. In the traditional setting of a countable numbe…
Aggregating Algorithm competing with Banach lattices
Fedor Zhdanov, Alexey Chernov, Yuri Kalnishkan
The paper deals with on-line regression settings with signals belonging to a Banach lattice. Our algorithms work in a semi-online setting where all the inputs are known in advance…
Linear Probability Forecasting
Fedor Zhdanov, Yuri Kalnishkan
Multi-class classification is one of the most important tasks in machine learning. In this paper we consider two online multi-class classification problems: classification by a lin…