PhD Thesis
Predictability of algorithmically random sequences,
Moscow State University, June 1988.
PhD Advisors Academician Andrei Kolmogorov and Professor Aleksei Semenov.
Books
Journal Publications
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On the concept of the Bernoulli property.
Russian Mathematical Surveys 41, 247248 (1986).
Another English translation
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On a randomness criterion.
Soviet Mathematics Doklady 35, 656660 (1987).
Another English translation
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Universal forecasting algorithms.
Information and Computation 96, 245277 (1992).
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On the empirical validity of the Bayesian method
(joint work with Vladimir V. V'yugin).
Journal of the Royal Statistical Society B 55,
253266 (1993).
-
A logic of probability,
with application to the foundations of statistics
(with discussion).
Journal of the Royal Statistical Society B 55,
317351 (1993).
-
A strictly martingale version
of Kolmogorov's strong law of large numbers.
Theory of Probability and Applications 41, no 3 (1996).
-
A game of prediction with expert advice.
Journal of Computer and System Sciences 56,
153173 (1998).
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Derandomizing stochastic prediction strategies.
Machine Learning 35,
247282 (1999).
-
Prequential probability:
principles and properties
(joint work with A Philip Dawid).
Bernoulli 5, 125162 (1999).
-
Competitive on-line statistics.
International Statistical Review 69,
213248 (2001).
-
Good randomized sequential probability forecasting is always possible
(joint work with Glenn Shafer).
Journal of the Royal Statistical Society B
67, 747763 (2005).
-
The sources of Kolmogorov's Grundbegriffe
(joint work with Glenn Shafer).
Statistical Science
21, 7098 (2006).
-
Hedging predictions in machine learning
(the second Computer Journal lecture,
with discussion, joint work with Alex Gammerman).
Computer Journal
50, 151177 (2007).
-
Competing with wild prediction rules.
Machine Learning
(Special Issue devoted to COLT 2006)
69, 193212 (2007).
-
On-line predictive linear regression
(joint work with Ilia Nouretdinov and Alex Gammerman).
Annals of Statistics
37, 15661590 (2009).
-
Superefficiency from the vantage point of computability.
Statistical Science
24, 7386 (2009).
-
Rough paths in idealized financial markets.
Lithuanian Mathematical Journal.
51, 274285 (2011).
-
Test martingales, Bayes factors, and p-values
(joint work with Glenn Shafer, Alexander Shen, and Nikolai Vereshchagin).
Statistical Science
26, 84101 (2011).
-
Lévy's zero-one law in game-theoretic probability
(joint work with Glenn Shafer and Akimichi Takemura).
Journal of Theoretical Probability
25, 124 (2012).
-
Continuous-time trading and the emergence of probability.
Finance and Stochastics
16, 561609 (2012).
Latest version
-
Ito calculus without probability in idealized financial markets.
Lithuanian Mathematical Journal
55, 270290 (2015).
-
Cross-conformal predictors.
Annals of Mathematics and Artificial Intelligence
(Special Issue on Conformal Prediction and its Applications)
74, 928 (2015).
-
Purely pathwise probability-free Ito integral.
Matematychni Studii 46, 96110 (2017).
-
Universal probability-free prediction
(joint work with Dusko Pavlovic).
Annals of Mathematics and Artificial Intelligence
(Special Issue on Conformal Prediction and its Applications)
81, 4770 (2017).
-
The role of measurability in game-theoretic probability.
Finance and Stochastics
21, 719739 (2017).
-
Nonparametric predictive distributions based on conformal prediction
(joint work with Jieli Shen, Valery Manokhin and Minge Xie).
Machine Learning
(Special Issue on Conformal Prediction)
108, 445474 (2019).
-
Combining p-values via averaging
(joint work with Ruodu Wang).
Biometrika, to appear.
Conferences
-
Aggregating strategies.
In:
Proceedings of the 3rd Annual Workshop
on Computational Learning Theory,
371383 (1990).
-
Learning by transduction
(joint work with Alex Gammerman and Vladimir Vapnik).
In:
Proceedings of the 14th Conference
on Uncertainty in Artificial Intelligence, 148156 (1998).
-
Ridge Regression Confidence Machine
(joint work with Ilia Nouretdinov and Tom Melluish).
In:
Proceedings of the 18th International Conference
on Machine Learning (2001).
-
On-line confidence machines are well-calibrated.
In:
Proceedings of the 43rd Annual Symposium on Foundations of Computer Science,
187196 (2002).
-
Testing exchangeability on-line
(joint work with Ilia Nouretdinov and Alex Gammerman).
Proceedings of the 20th International Conference on Machine Learning,
768775 (2003).
-
Self-calibrating probability forecasting
(joint work with Glenn Shafer and Ilia Nouretdinov).
In:
Advances in Neural Information Processing Systems 16
(2004).
-
Defensive forecasting
(joint work with Akimichi Takemura and Glenn Shafer).
In:
Proceedings of the 10th International Workshop
on Artificial Intelligence and Statistics,
365372 (2005).
Available electronically at
http://www.gatsby.ucl.ac.uk/aistats/.
-
Conditional prediction intervals for linear regression
(joint work with Peter McCullagh, Ilia Nouretdinov, Dmitry Devetyarov, and Alex Gammerman).
In:
Proceedings of the International Conference on Machine Learning and Applications,
131138 (2009).
-
Efficiency of conformalized ridge regression
(joint work with Evgeny Burnaev).
In:
Proceedings of the 27th Annual Conference on Learning Theory,
JMLR: Workshop and Conference Proceedings
35, 605622 (2014).
-
VennAbers predictors
(joint work with Ivan Petej).
In:
Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence,
829838 (2014).
-
Large-scale probabilistic predictors with and without guarantees of validity
(joint work with Ivan Petej and Valentina Fedorova).
In:
Advances in Neural Information Processing Systems 28
892900 (2015).
-
Universally consistent conformal predictive distributions.
In:
Proceedings of the 8th Symposium on Conformal and Probabilistic Prediction with Applications,
Proceedings of Machine Learning Research
105 105122 (2019).
Technical Reports
See arXiv
technical reports.
Last modified on 3 June 2020