Vladimir Vovk's publications

Vladimir Vovk's selected publications

PhD Thesis

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

Books

Probability and finance: it's only a game (with Glenn Shafer), New York: Wiley, 2001. Japanese translation: Tokyo: Iwanami Shoten, 2006.
Algorithmic learning in a random world (with Alex Gammerman and Glenn Shafer), New York: Springer, 2005. Second edition: to appear in 2022.
Game-theoretic foundations for probability and finance (with Glenn Shafer), Hoboken, NJ: Wiley, 2019.

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 on-line 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).
On-line 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 p-values (joint work with Glenn Shafer, Alexander Shen, and Nikolai Vereshchagin). Statistical Science 26, 84–101 (2011).
Lévy's zero-one law in game-theoretic probability (joint work with Glenn Shafer and Akimichi Takemura). Journal of Theoretical Probability 25, 1–24 (2012).
Continuous-time 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).
Cross-conformal predictors. Annals of Mathematics and Artificial Intelligence (Special Issue on Conformal Prediction and its Applications) 74, 9–28 (2015).
Purely pathwise probability-free Ito integral. Matematychni Studii 46, 96–110 (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, 47–70 (2017).
The role of measurability in game-theoretic 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 p-values via averaging (joint work with Ruodu Wang). Biometrika 107, 791–808 (2020).
E-values: 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 p-values under arbitrary dependence (joint work with Bin Wang and Ruodu Wang). Annals of Statistics 50, 351–375 (2022).
Confidence and discoveries with e-values (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).
On-line confidence machines are well-calibrated. In: Proceedings of the 43rd Annual Symposium on Foundations of Computer Science, 187–196 (2002).
Testing exchangeability on-line (joint work with Ilia Nouretdinov and Alex Gammerman). Proceedings of the 20th International Conference on Machine Learning, 768–775 (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, 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).
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 28 892–900 (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