Vladimir Vovk's selected publications

PhD Thesis

Books

• Probability and finance: it's only a game (with Glenn Shafer), Wiley, New York, 2001. Japanese translation: Iwanami Shoten, Tokyo, 2006.
• Algorithmic learning in a random world (with Alex Gammerman and Glenn Shafer), Springer, New York, 2005. Second edition: Springer, Cham, 2022.
• Game-theoretic foundations for probability and finance (with Glenn Shafer), Wiley, Hoboken, NJ, 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 38, 329–354 (2023).

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