Vladimir Vovk's selected publications

PhD Thesis

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.

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). To appear.
• The role of measurability in game-theoretic probability. Finance and Stochastics. To appear.

Conferences

• Aggregating strategies. In: Proceedings of the 3rd Annual Workshop on Computational Learning Theory, 371–383. San Mateo, CA: Morgan Kaufmann (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. San Francisco, CA: Morgan Kaufmann (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. Los Alamitos, CA: IEEE Computer Society (2002).
• Testing exchangeability on-line (joint work with Ilia Nouretdinov and Alex Gammerman). Proceedings of the 20th International Conference on Machine Learning, 768–775. Menlo Park, CA: AAAI Press (2003).
• Self-calibrating probability forecasting (joint work with Glenn Shafer and Ilia Nouretdinov). In: Advances in Neural Information Processing Systems 16. 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, 365–372. Society for Artificial Intelligence and Statistics (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 Twenty Seventh Annual Conference on Learning Theory, JMLR: Workshop and Conference Proceedings 35, 605–622 (2014).

Technical Reports