Yuri Kalnishkan

Address

Department of Computer Science
Royal Holloway, University of London
Egham
Surrey
TW20 0EX
United Kingdom

Teaching

1. CS3930, Computational Finance: 2008/09 course page
2. CS2630, Operating Systems: 2008/09 course page (taught jointly with Prof. Gregory Gutin).

PhD Students

• Steve Busuttil (successful viva on 29 May 2008). Dissertation: The Aggregating Algorithm and Regression, 776Kb, pdf.

Publications

In Journals

1. Y.Kalnishkan and M.V.Vyugin. The weak aggregating algorithm and weak mixability. Journal of Computer and System Sciences, 74(8): 1228--1244 (2008).
2. Y.Kalnishkan, V.Vovk, and M.V.Vyugin. How many strings are easy to predict? Information and Computation, 201: 55--71 (2005).
3. Y.Kalnishkan, V.Vovk, and M.V.Vyugin. Loss functions, complexities, and the Legendre transformation. Theoretical Computer Science, 313(2): 195-207, (2004).
4. Y.Kalnishkan. General linear relations among different types of predictive complexity. Theoretical Computer Science, 271(1-2): 181--200, (2002).

In Refereed Conference Proceedings

1. S.Busuttil and Y.Kalnishkan. Weighted Kernel Regression for Predicting Changing Dependencies. In Machine Learning: ECML 2007, 18th European Conference on Machine Learning, volume 4701 of Lecture Notes in Computer Science, pages 535--542. Springer, 2007.
2. Y.Kalnishkan, V.Vovk and M.V.Vyugin. Generalised Entropy and Asymptotic Complexities of Languages. In Learning Theory, 20th Annual Conference on Learning Theory, COLT 2007, volume 4539 of Lecture Notes in Computer Science, pages 293--307, Springer 2007.
3. S.Busuttil, Y.Kalnishkan, and A.Gammerman. Improving the aggregating algorithm for regression. In IASTED International Conference on Artificial Intelligence and Applications, pages 379--384, IASTED/ACTA Press 2007.
4. Y.Kalnishkan and M.V.Vyugin. The Weak Aggregating Algorithm and Weak Mixability. In Learning Theory, Proceedings of the 18th Annual Conference (COLT 2005), volume 3559 of Lecture Notes in Artificial Intelligence, Springer, 2005.
5. Y.Kalnishkan, V.Vovk, and M.V. Vyugin. A Criterion for the Existence of Predictive Complexity for Binary Games. In Algorithmic Learning Theory, 15th International Conference, ALT 2004, Proceedings, volume 3244 of Lecture Notes in Artificial Intelligence, pages 249--263. Springer, 2004.
6. A.Gammerman, Y.Kalnishkan, and V.Vovk. On-line Prediction with Kernels and the Complexity Approximation Principle. In Uncertainty in Artificial Intelligence, Proceedings of the Twentieth Conference, pages 170--176. AUAI Press, 2004.
7. Y.Kalnishkan, V.Vovk and M.V.Vyugin. How Many Strings Are Easy to Predict? In 16th Annual Conference on Learning Theory (COLT) and 7th Annual Workshop on Kernel Machines, Proceedings, volume 2777 of Lecture Notes in Artificial Intelligence, Springer-Verlag, 2003.
8. Y.Kalnishkan and M.V.Vyugin. On the Absence of Predictive Complexity for Some Games. In Algorithmic Learning Theory 13th International Conference, ALT 2002, Proceedings,, volume 2533 of Lecture Notes in Artificial Intelligence, Springer-Verlag, 2002.
9. Y.Kalnishkan and M.V.Vyugin. Mixability and the Existence of Weak Complexities. In Computational Learning Theory, 15th Annual Conference on Computational Learning Theory, COLT 2002, Proceedings, volume 2375 of Lecture Notes in Artificial Intelligence, pages 105--120. Springer, 2002.
10. Y.Kalnishkan, M.V.Vyugin and V.Vovk. Loss Functions, Complexities, and the Legendre Transformation. In Algorithmic Learning Theory 12th International Conference, ALT 2001, Proceedings, volume 2225 of Lecture Notes in Artificial Intelligence, pages 181--189. Springer-Verlag, 2001.
11. Y.Kalnishkan. Complexity Approximation Principle and Rissanen's Approach to Real-Valued Parameters. In Machine Learning: ECML 2000, 11th European Conference on Machine Learning, Proceedings, volume 1810 of Lecture Notes in Artificial Intelligence, pages 203--210, Springer-Verlag, 2000.
12. Y.Kalnishkan. General Linear Relations among Different Types of Predictive Complexity. In Algorithmic Learning Theory, 10th International Conference, ALT'99, Proceedings pages 323--334, volume 1720 of Lecture Notes in Artificial Intelligence, Springer-Verlag, 1999.
13. Y.Kalnishkan. Linear Relations between Square-Loss and Kolmogorov Complexity. In Proceedings of the Twelfth Annual Conference on Computation Learning Theory, pages 226--232. Association for Computing Machinery, 1999.

Technical Reports

1. S.Busuttil, Y.Kalnishkan and A.Gammerman. Two New Kernel Least Squares Based Methods for Regression. Technical Report CLRC-TR-06-01, Computer Learning Research Centre, Royal Holloway, University of London, March 2006. Download: pdf.
2. Y.Kalnishkan, V.Vovk, and M.V.Vyugin. A Criterion for the Existence of Predictive Complexity for Binary Games. Technical Report CLRC-TR-04-04, Computer Learning Research Centre, Royal Holloway, University of London, March 2004, revised May 2004. Download: postscript.
3. Y.Kalnishkan and M.V.Vyugin. The Weak Aggregating Algorithm and Weak Mixability. Technical Report CLRC-TR-03-01, Computer Learning Research Centre, Royal Holloway, University of London, November 2003. Download: postscript.
4. Y.Kalnishkan and V.Vovk. The existence of predictive complexity and the Legendre transformation. Technical report CLRC-TR-00-04, Computer Learning Research Centre, Royal Holloway College, May 2000. Presented at TAI 2000, Fourth French Days on Algorithmic Information Theory. Download: postscript.

See Also

A list of my papers on dblp.

Dissertation

The viva for the doctoral dissertation 'The Aggregating Algorithm and Predictive Complexity' took place on the 1st of October, 2002. Advisers: Volodya Vovk and Alex Gammerman. Examiners: Peter Gacs and Paul Vitanyi. Download: zipped postscript (413 KB) or pdf (543 KB). The dissertation is also available on the ECCC (see this page for the abstract, table of contents, and another copy of the full text).

Grants and Awards