Royal Holloway logo and departmental theme Royal Holloway, University of London

Yuri Kalnishkan

Yura Kalnishkan
Position Lecturer in Computer Science
Email yura (at) cs (dot) rhul (dot) ac (dot) uk
Phone (+44) 1784 41 4256
Fax (+44) 1784 43 9786
Research Area computational learning, on-line prediction, aggregating algorithm, predictive and Kolmogorov complexity.
CV in pdf format (August 2014)

Address

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

Teaching

  1. CS3930, Computational Finance, Term 1
  2. CS5200, On-line Learning (with Dr. Zhiyuan Luo), Term 2

PhD Students

Recent Talks

Articles on OnlinePrediction.net

Survey articles I have written for the wiki project http://onlineprediction.net:

Publications

In Journals

  1. Y.Kalnishkan, V.Vyugin, and V.Vovk. Generalised Entropies and Asymptotic Complexities of Languages. Information and Computation, 237, 101-141 (2014). DOI, Pure.
  2. F.Zhdanov and Y.Kalnishkan. An Identity for Kernel Ridge Regression. Theoretical Computer Science, 473, 157-178 (2013). DOI, Pure.
  3. F.Zhdanov and Y.Kalnishkan. Universal Algorithms for Probability Forecasting. International Journal on Artificial Intelligence Tools, 21(4) (2012). DOI, Pure.
  4. A.Chernov, Y.Kalnishkan, F.Zhdanov, and V.Vovk. Supermartingales in Prediction with Expert Advice. Theoretical Computer Science, 411(29-30): 2647--2669 (2010). DOI, Pure. See also the arXiv.org version.
  5. Y.Kalnishkan and M.V.Vyugin. The weak aggregating algorithm and weak mixability. Journal of Computer and System Sciences, 74(8): 1228--1244 (2008). DOI, Pure.
  6. Y.Kalnishkan, V.Vovk, and M.V.Vyugin. How many strings are easy to predict? Information and Computation, 201: 55--71 (2005). DOI.
  7. Y.Kalnishkan, V.Vovk, and M.V.Vyugin. Loss functions, complexities, and the Legendre transformation. Theoretical Computer Science, 313(2): 195-207, (2004). DOI.
  8. Y.Kalnishkan. General linear relations among different types of predictive complexity. Theoretical Computer Science, 271(1-2): 181--200, (2002). DOI.

In Refereed Conference Proceedings

  1. T.Scarfe, W.Koolen, and Y.Kalnishkan. A long-range self-similarity approach to segmenting DJ mixed music streams. In Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, Springer, 2013. p. 235-244. DOI, Pure.
  2. F.Zhdanov and Y.Kalnishkan. An Identity for Kernel Ridge Regression. In Algorithmic Learning Theory 21st International Conference, ALT 2010, Proceedings, volume 6331 of Lecture Notes in Computer Science, pages 405-419. Springer, 2010. DOI.
  3. F.Zhdanov and Y.Kalnishkan. Linear Probability Forecasting. In Artificial Intelligence Applications and Innovations, AIAI 2010, Proceedings, volume 339 of IFIP Advances in Information and Communication Technology, pages 4-11. Springer, 2010. DOI.
  4. A.Chernov, Y.Kalnishkan, F.Zhdanov, and V.Vovk. Supermartingales in Prediction with Expert Advice. In Algorithmic Learning Theory, 19th International Conference, ALT 2008, Proceedings, volume 5254 of Lecture Notes in Computer Science, pages 199-213. Springer, 2008. DOI.
  5. S.Busuttil and Y.Kalnishkan. On-line Regression Competitive with Changing Predictors. In Algorithmic Learning Theory, 18th International Conference, ALT 2007, Proceedings, volume 4754 of Lecture Notes in Computer Science, pages 181-195. Springer, 2007. DOI.
  6. 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. DOI.
  7. 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. DOI.
  8. The statement of the main theorem in this version of the paper is inaccurate. See the journal version from Information and Computation, 2014 for the correct theorem.
  9. 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. DOI.
  10. 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. DOI.
  11. 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. ACM Digital Library.
  12. 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. DOI.
  13. 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. DOI.
  14. 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. DOI.
  15. 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. DOI.
  16. 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. DOI.
  17. 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. DOI.
  18. 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. F.Zhdanov and Y.Kalnishkan. An Identity for Kernel Ridge Regression. arXiv:1112.1390
  2. 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.
  3. 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.
  4. 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.
  5. 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

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).

Research Grants

Date Grant Body
2013-2016 Grant RPG-2013-047 'Online self-tuning learning algorithms for handling historical information' Leverhulme Trust (awards 2013)
2007-2010 Co-investigator on the grant EP/F002998 'Practical competitive prediction' (with Profs. V.Vovk and A.Gammerman) Engineering and Physical Sciences Research Council
2001-2003 Researcher Co-investigator on the grant GR/R46670 'Complexity Approximation Principle and Predictive Complexity: Analysis and Applications' (held by Profs. A.Gammerman and V.Vovk) Engineering and Physical Sciences Research Council
1998-2001 PhD funded by the grant GR/M14937 'Predictive Complexity: recursion-theoretic variants' (held by Prof. V.Vovk) Engineering and Physical Sciences Research Council
 

Academic Awards

Date Award Body
2010 Best paper award (with F.Zhdanov) 6th IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2011
1998-2001 Overseas Research Students Awards Scheme grant Committee of Vice-Chancellors and Principals of the Universities of the United Kingdom
2000 BrainBuster competition in MATLAB programming, first prize ECM/MathWorks
1999 E Mark Gold Award The program committee of the 10th International Conference on Algorithmic Learning Theory (Tokyo, Japan)
 

Back To:


Last updated Tue, 5-Mar-2013
Department of Computer Science, University of London, Egham, Surrey TW20 0EX
Tel/Fax : +44 (0)1784 443421 /439786