PhD Thesis
Predictability of algorithmically random sequences,
Moscow State University, June 1988.
PhD Advisors Academician Andrei Kolmogorov and Professor Aleksei Semenov.
Books
Journal Publications

On the concept of the Bernoulli property.
Russian Mathematical Surveys 41, 247–248 (1986).
Another English translation

On a randomness criterion.
Soviet Mathematics Doklady 35, 656–660 (1987).
Another English translation

Universal forecasting algorithms.
Information and Computation 96, 245–277 (1992).

On the empirical validity of the Bayesian method
(joint work with Vladimir V. V'yugin).
Journal of the Royal Statistical Society B 55,
253–266 (1993).

A logic of probability,
with application to the foundations of statistics
(with discussion).
Journal of the Royal Statistical Society B 55,
317–351 (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,
153–173 (1998).

Derandomizing stochastic prediction strategies.
Machine Learning 35,
247–282 (1999).

Prequential probability:
principles and properties
(joint work with A Philip Dawid).
Bernoulli 5, 125–162 (1999).

Competitive online statistics.
International Statistical Review 69,
213–248 (2001).

Good randomized sequential probability forecasting is always possible
(joint work with Glenn Shafer).
Journal of the Royal Statistical Society B
67, 747–763 (2005).

The sources of Kolmogorov's Grundbegriffe
(joint work with Glenn Shafer).
Statistical Science
21, 70–98 (2006).

Hedging predictions in machine learning
(the second Computer Journal lecture,
with discussion, joint work with Alex Gammerman).
Computer Journal
50, 151–177 (2007).

Competing with wild prediction rules.
Machine Learning
(Special Issue devoted to COLT 2006)
69, 193–212 (2007).

Online predictive linear regression
(joint work with Ilia Nouretdinov and Alex Gammerman).
Annals of Statistics
37, 1566–1590 (2009).

Superefficiency from the vantage point of computability.
Statistical Science
24, 73–86 (2009).

Rough paths in idealized financial markets.
Lithuanian Mathematical Journal.
51, 274–285 (2011).

Test martingales, Bayes factors, and pvalues
(joint work with Glenn Shafer, Alexander Shen, and Nikolai Vereshchagin).
Statistical Science
26, 84–101 (2011).

Lévy's zeroone law in gametheoretic probability
(joint work with Glenn Shafer and Akimichi Takemura).
Journal of Theoretical Probability
25, 1–24 (2012).

Continuoustime trading and the emergence of probability.
Finance and Stochastics
16, 561–609 (2012).
Latest version

Ito calculus without probability in idealized financial markets.
Lithuanian Mathematical Journal
55, 270–290 (2015).

Crossconformal predictors.
Annals of Mathematics and Artificial Intelligence
(Special Issue on Conformal Prediction and its Applications)
74, 9–28 (2015).

Purely pathwise probabilityfree Ito integral.
Matematychni Studii 46, 96–110 (2017).

Universal probabilityfree prediction
(joint work with Dusko Pavlovic).
Annals of Mathematics and Artificial Intelligence
(Special Issue on Conformal Prediction and its Applications)
81, 47–70 (2017).

The role of measurability in gametheoretic probability.
Finance and Stochastics
21, 719–739 (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, 445–474 (2019).

Combining pvalues via averaging
(joint work with Ruodu Wang).
Biometrika
107, 791–808 (2020).

Evalues: calibration, combination, and applications
(joint work with Ruodu Wang).
Annals of Statistics
49, 1736–1754 (2021).

Testing randomness online.
Statistical Science
36, 595–611 (2021).

Universal predictive systems.
Pattern Recognition
(Special Issue on Conformal Prediction)
126, 108536 (2022).

Admissible ways of merging pvalues under arbitrary dependence
(joint work with Bin Wang and Ruodu Wang).
Annals of Statistics
50, 351–375 (2022).

Confidence and discoveries with evalues
(joint work with Ruodu Wang).
Statistical Science, to appear.
Conferences

Aggregating strategies.
In:
Proceedings of the 3rd Annual Workshop
on Computational Learning Theory,
371–383 (1990).

Learning by transduction
(joint work with Alex Gammerman and Vladimir Vapnik).
In:
Proceedings of the 14th Conference
on Uncertainty in Artificial Intelligence, 148–156 (1998).

Ridge Regression Confidence Machine
(joint work with Ilia Nouretdinov and Tom Melluish).
In:
Proceedings of the 18th International Conference
on Machine Learning (2001).

Online confidence machines are wellcalibrated.
In:
Proceedings of the 43rd Annual Symposium on Foundations of Computer Science,
187–196 (2002).

Testing exchangeability online
(joint work with Ilia Nouretdinov and Alex Gammerman).
Proceedings of the 20th International Conference on Machine Learning,
768–775 (2003).

Selfcalibrating 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,
365–372 (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,
131–138 (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, 605–622 (2014).

Venn–Abers predictors
(joint work with Ivan Petej).
In:
Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence,
829–838 (2014).

Largescale probabilistic predictors with and without guarantees of validity
(joint work with Ivan Petej and Valentina Fedorova).
In:
Advances in Neural Information Processing Systems 28
892–900 (2015).

Conformal changepoint detection in a binary model situation.
In:
Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction with Applications,
Proceedings of Machine Learning Research
152 131–150 (2021).
Technical Reports
See arXiv
technical reports.
Last modified on 23 October 2022