(some of them are available elsewhere on this web site)
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).

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

The law of the iterated logarithm for random Kolmogorov,
or chaotic, sequences.
Theory of Probability and Applications 32, 413–425 (1987).

Kolmogorov–Stout law of the iterated logarithm.
Mathematical Notes 44, 502–507 (1988).

Prediction of stochastic sequences.
Problems of Information Transmission 25,
285–296 (1989).

Asymptotic efficiency of estimators:
an algorithmic approach.
Theory of Probability and Applications 36, 329–343 (1991).

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).

Forecasting point and continuous processes:
prequential analysis.
Test 2, 189–217 (1993).

Prequential level of impossibility with some applications
(joint work with Vladimir V V'yugin).
Journal of the Royal Statistical Society B 56,
115–123 (1994).

A strictly martingale version
of Kolmogorov's strong law of large numbers.
Theory of Probability and Applications 41, no 3 (1996).

Learning about the parameter of the Bernoulli model.
Journal of Computer and System Sciences 55, 96–104 (1997).

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).

Editorial—Kolmogorov complexity
(joint work with Alex Gammerman).
Computer Journal 42, 251 (1999).

Kolmogorov complexity:
sources, theory and applications
(joint work with Alex Gammerman).
Computer Journal 42,
252–255 (1999).

Complexity Approximation Principle
(joint work with Alex Gammerman).
Computer Journal 42,
318–322 (1999).

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

Probability theory for the Brier game.
Theoretical Computer Science 261, 57–79 (2001).

Predicting nearly as well as the best pruning
of a decision tree
through dynamic programming scheme
(joint work with Eiji Takimoto and Akira Maruoka).
Theoretical Computer Science 261,
179–209 (2001).

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

Kolmogorov's contributions to the foundations of probability.
Problems of Information Transmission 39,
21–31 (2003).

Loss functions, complexities and the Legendre transformation
(joint work with Yuri Kalnishkan and Misha Vyugin).
Theoretical Computer Science 313,
195–207 (2004).

Universal wellcalibrated algorithm for online classification.
Journal of Machine Learning Research,
5, 575–604 (2004).

How many strings are easy to predict?
(joint work with Yuri Kalnishkan and Misha Vyugin).
Information and Computation,
201, 55–71 (2005).
Conference version: COLT 2003.

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

Wellcalibrated predictions from online compression models.
Theoretical Computer Science
(Special Issue devoted to ALT 2003)
364, 10–26 (2006).

Criterion of calibration for Transductive Confidence Machine
with limited feedback
(joint work with Ilia Nouretdinov).
Theoretical Computer Science
(Special Issue devoted to ALT 2003)
364, 3–9 (2006).

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).

Nonasymptotic calibration and resolution.
Theoretical Computer Science
(Special Issue devoted to ALT 2005)
387, 77–89 (2007).

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

Leading strategies in competitive online learning.
Theoretical Computer Science
(Special Issue devoted to ALT 2006)
405, 285–296 (2008).

The gametheoretic capital asset pricing model
(joint work with Glenn Shafer).
International Journal of Approximate Reasoning
49, 175–197 (2008).

A tutorial on conformal prediction
(joint work with Glenn Shafer).
Journal of Machine Learning Research
9, 371–421 (2008).

Continuoustime trading and the emergence of volatility.
Electronic Communications in Probability
13, 319–324 (2008).

Merging of opinions in gametheoretic probability.
Annals of the Institute of Statistical Mathematics
61, 969–993 (2009).

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

Continuoustime trading and the emergence of randomness.
Stochastics
81, 455–466 (2009).

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

Prediction with expert advice for the Brier game
(joint work with Fedor Zhdanov).
Journal of Machine Learning Research
10, 2413–2439 (2009).

Prequential randomness and probability
(joint work with Alexander Shen).
Theoretical Computer Science
(Special Issue devoted to ALT 2008)
411, 2632–2646 (2010).

Supermartingales in prediction with expert advice
(joint work with Alexey Chernov, Yuri Kalnishkan, and Fedor Zhdanov).
Theoretical Computer Science
(Special Issue devoted to ALT 2008)
411, 2647–2669 (2010).

Weak Aggregating Algorithm
for the distributionfree perishable inventory problem
(joint work with Tatsiana Levina, Yuri Levin, Jeff McGill, and Mikhail Nediak).
Operations Research Letters
38, 516–521 (2010).

Insuring against loss of evidence in gametheoretic probability
(joint work with A Philip Dawid, Steven de Rooij, Glenn Shafer,
Alexander Shen, and Nikolai Vereshchagin).
Statistics and Probability Letters
81, 157–162 (2011).

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).

The generality of the zeroone laws
(joint work with Akimichi Takemura and Glenn Shafer).
Annals of the Institute of Statistical Mathematics
63, 873–885 (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).
Conferences

Aggregating strategies.
In:
Proceedings of the 3rd Annual Workshop
on Computational Learning Theory
(ed by M Fulk and J Case),
pp 371–383.
San Mateo, CA: Morgan Kaufmann, 1990.

An optimalcontrol application
of two paradigms of online learning.
In:
Proceedings of the 7th Annual Workshop
on Computational Learning Theory
(ed by M K Warmuth),
pp 98–109.
New York: ACM Press, 1994.

Minimum description length estimators
under the optimal coding scheme.
In:
Computational learning theory
(ed by P Vitanyi),
Lecture Notes in Computer Science, vol 904, pp 237–251.
Berlin: Springer, 1995.

Learning an optimal decision strategy
in an influence diagram with latent variables.
In:
Proceedings of the 9th Annual Conference
on Computational Learning Theory,
pp 110–121.
1996.

Universal portfolio selection
(joint work with Chris Watkins).
In:
Proceedings of the 11th Annual Conference
on Computational Learning Theory, pp 12–23.
1998.

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

Ridge Regression learning algorithm in dual variables
(joint work with Craig Saunders and Alex Gammerman).
In:
Proceedings of the 15th International Conference on Machine Learning
(ed by J W Shavlik),
pp 515–521.
San Francisco, CA: Morgan Kaufmann, 1998.

Machinelearning applications of algorithmic randomness
(joint work with Craig Saunders and Alex Gammerman).
In:
Proceedings of the 16th International Conference
on Machine Learning, p 444–453.
1999.

Transduction with confidence and credibility
(joint work with Craig Saunders and Alex Gammerman).
In:
Proceedings of the 16th International Joint Conference
on Artificial Intelligence, pp 722–726.
1999.

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

Pattern recognition and density estimation
under the general iid assumption
(joint work with Ilia Nouretdinov, Michael Vyugin and Alex Gammerman).
In:
Proceedings of the 14th Annual Conference
on Computational Learning Theory
and 5th European Conference
on Computational Learning Theory
(ed by D Helmbold and B Williamson),
Lecture Notes in Artificial Intelligence, vol 2111,
pp 337–353.
2001.

Comparing the Bayes and typicalness frameworks
(joint work with Tom Melluish, Craig Saunders and Ilia Nouretdinov).
In:
Machine Learning: ECML 2001.
Proceedings of the 12th European Conference on Machine Learning
(ed by L De Raedt and P Flash),
Lecture Notes in Artificial Intelligence, vol 2167,
pp 360–371.
2001.

Online confidence machines are wellcalibrated.
In:
Proceedings of the 43rd Annual Symposium on Foundations of Computer Science,
pp 187–196.
Los Alamitos, CA: IEEE Computer Society, 2002.

Asymptotic optimality of Transductive Confidence Machine.
In:
Proceedings of the 13th International Conference on Algorithmic Learning Theory
(ed by N CesaBianchi, M Numao and R Reischuk),
Lecture Notes in Artificial Intelligence, vol 2533, pp 336–350.
2002.

Universal wellcalibrated algorithm for online classification.
In:
Learning Theory and Kernel Machines.
Proceedings of the 16th Annual Conference on Learning Theory
and Seventh Kernel Workshop, COLT/Kernel 2003
(ed by B Schölkopf and M K Warmuth),
Lecture Notes in Artificial Intelligence, vol 2777, pp 358–372.
Berlin: Springer, 2003.

Testing exchangeability online
(joint work with Ilia Nouretdinov and Alex Gammerman).
Proceedings of the 20th International Conference on Machine Learning
(ed by T Fawcett and N Mishra),
pp 768–775.
Menlo Park, CA: AAAI Press, 2003.

Online prediction with kernels and the Complexity Approximation Principle
(joint work with Alex Gammerman and Yura Kalnishkan).
In:
Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence
(ed by M Chickering and J Halpern),
pp 170–176.
Arlington, VA: AUAI Press, 2004.

A criterion for the existence of predictive complexity for binary games
(joint work with Yuri Kalnishkan and Misha Vyugin).
In:
Proceedings of the 15th International Conference
on Algorithmic Learning Theory
(ed by S BenDavid, J Case and A Maruoka),
Lecture Notes in Artificial Intelligence,
vol 3244, pp 249–263,
Heidelberg: Springer, 2004.
(Full version:
Technical Report CLRCTR0404,
Computer Learning Research Centre, Royal Holloway, University of London.)

Selfcalibrating probability forecasting
(joint work with Glenn Shafer and Ilia Nouretdinov).
In:
Advances in Neural Information Processing Systems 16
(ed by S Thrun, L K Saul, B Schölkopf).
Cambridge, MA: MIT Press, 2004.

Defensive forecasting
(joint work with Akimichi Takemura and Glenn Shafer).
In:
Proceedings of the 10th International Workshop
on Artificial Intelligence and Statistics
(ed by R G Cowell and Z Ghahramani),
pp 365–372.
Society for Artificial Intelligence and Statistics,
2005.
Available electronically at
http://www.gatsby.ucl.ac.uk/aistats/.

Defensive prediction with expert advice.
In:
Proceedings of the 16th International Conference
on Algorithmic Learning Theory
(ed by S Jain, H U Simon and E Tomita),
Lecture Notes in Computer Science,
vol 3734, pp 444–458.
Berlin: Springer, 2005.

Defensive forecasting for linear protocols
(joint work with Ilia Nouretdinov, Akimichi Takemura and Glenn Shafer).
In:
Proceedings of the 16th International Conference
on Algorithmic Learning Theory
(ed by S Jain, H U Simon and E Tomita),
Lecture Notes in Computer Science,
vol 3734, pp 444–458.
Berlin: Springer, 2005.

Online regression competitive with reproducing kernel Hilbert spaces
(extended abstract).
In:
Theory and Applications of Models of Computation.
Proceedings of the 3rd Annual Conference on Computation and Logic
(ed by JY Cai, S B Cooper and A Li),
Lecture Notes in Computer Science,
vol 3959, pp 452–463.
Berlin: Springer, 2006.

Competing with stationary prediction strategies.
In:
Proceedings of the 20th Annual Conference on Learning Theory
(ed by N Bshouty and C Gentile),
Lecture Notes in Artificial Intelligence,
vol 4539, pp 439–453.
Berlin: Springer, 2007.

Generalized entropies and asymptotic complexities of languages
(joint work with Yura Kalnishkan and Misha Vyugin).
In:
Proceedings of the 20th Annual Conference on Learning Theory
(ed by N Bshouty and C Gentile),
Lecture Notes in Artificial Intelligence,
vol 4539, pp 293–307,
Berlin: Springer, 2007.

Prediction with expert evaluators' advice
(joint work with Alexey Chernov).
In:
Proceedings of the 20th International Conference
on Algorithmic Learning Theory
(ed by R Gavaldà, G Lugosi, T Zeugmann, and S Zilles),
Lecture Notes in Artificial Intelligence,
vol 5809, pp 8–22,
Berlin: Springer, 2009.

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,
pp 131–138, 2009.

Prediction with advice of unknown number of experts
(joint work with Alexey Chernov).
In:
Proceedings of the 26th Conference
on Uncertainty in Artificial Intelligence
(ed by P Grünwald and P Spirtes),
pp 117–125.
Arlington, VA: AUAI Press, 2010.

Competitive online generalized linear regression under square loss
(joint work with Fedor Zhdanov).
In:
Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery
in Databases 2010.

Plugin martingales for testing exchangeability online
(joint work with Valentina Fedorova, Ilia Nouretdinov, and Alex Gammerman).
In:
Proceedings of the 29th International Conference on Machine Learning
(ed by J Langford and J Pineau),
arXiv:1207.4676v1 [cs.LG],
July 2012.

Buy low, sell high
(joint work with Wouter M Koolen).
In:
Proceedings of the 23rd International Conference
on Algorithmic Learning Theory
(ed by N Bshouty, G Stoltz, N Vayatis, and T Zeugmann),
Lecture Notes in Artificial Intelligence,
to appear.
Berlin: Springer, 2012.

A closer look at adaptive regret
(joint work with Dmitry Adamskiy, Wouter M Koolen, and Alexey Chernov).
In:
Proceedings of the 23rd International Conference
on Algorithmic Learning Theory
(ed by N Bshouty, G Stoltz, N Vayatis and T Zeugmann),
Lecture Notes in Artificial Intelligence,
to appear.
Berlin: Springer, 2012.

Inductive conformal predictors in the batch mode.
In:
Proceedings of the 4th Asian Conference
on Machine Learning
(ed by S C H Hoi and W Buntine),
JMLR: Workshop and Conference Proceedings,
to appear in 2012.
Book Chapters

Another semantics for Pearl's action calculus.
In: Computational Learning and Probabilistic Reasoning
(ed by A Gammerman),
pp 127–146 (Chapter 7).
New York: Wiley, 1995.

Kolmogorov's complexity conception of probability.
In:
Statistics—Philosophy, Recent History and Relations to Science
(ed by V F Hendricks, S A Pedersen, and K F Jørgensen),
pp 51–69.
Dordrecht: Kluwer, 2001.
Technical Reports

Finitary prequential probability: asymptotic results.
Institute of New Technologies, Moscow, May 1991.

Online learning in a finitestate environment:
decision theoretic approach.
Independent University of Moscow,
Technical Report V931, December 1993.

See also arXiv
technical reports.
Last modified on 17 September 2012