Universal portfolio selection

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Universal portfolio selection

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Annual Workshop on Computational Learning Theory archive
Proceedings of the eleventh annual conference on Computational learning theory table of contents

Madison, Wisconsin, United States

Pages: 12 - 23

Year of Publication: 1998

ISBN:1-58113-057-0

Authors

V. Vovk	Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK
C. Watkins	Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK

Sponsors

University of Wisconsin : University of Wisconsin
UC @ Santa Cruz : UC @ Santa Cruz
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 8, Downloads (12 Months): 62, Citation Count: 14

Additional Information:

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Avrim Blum , Adam Kalai, Universal portfolios with and without transaction costs, Proceedings of the tenth annual conference on Computational learning theory, p.309-313, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267518]
	2	T. Cover and E. Ordentlich. Universal portfolios with side information. IEEE Trans. Inform. Theory, 42:348 363, 1996.
	3	M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. IEEE Trans. Inform. Theory, 38:1258-1270, 1992.
	4	G. H. Hardy, J. E. Littlewood, and G. P61ya. Inequalities. Cambridge University Press, Cambridge, 1967.
	5	David Haussler , Jyrki Kivinen , Manfred K. Warmuth, Tight worst-case loss bounds for predicting with expert advice, Proceedings of the Second European Conference on Computational Learning Theory, p.69-83, March 13-15, 1995
	6	D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. On-line portfolio selection using multiplicative updates. In Proceedings of the 13th International Conference on Machine Learning, 1996.
	7	M. Herbster and M. Warmuth. Tracking the best expert. In Proceedings of the 12th International Conference on Machine Learning, pages 286-294. Morgan Kaufmann, 1995.
	8	Jyrki Kivinen , Manfred K. Warmuth, Exponentiated gradient versus gradient descent for linear predictors, Information and Computation, v.132 n.1, p.1-63, Jan. 10, 1997 [doi>10.1006/inco.1996.2612]
	9	Ming Li , Paul Vitányi, An introduction to Kolmogorov complexity and its applications (2nd ed.), Springer-Verlag New York, Inc., Secaucus, NJ, 1997
	10	Nicholas Littlestone , Philip M. Long , Manfred K. Warmuth, On-line learning of linear functions, Computational Complexity, v.5 n.1, p.1-23, Jan. 1995 [doi>10.1007/BF01277953]
	11	Nick Littlestone , Manfred K. Warmuth, The weighted majority algorithm, Information and Computation, v.108 n.2, p.212-261, Feb. 1, 1994 [doi>10.1006/inco.1994.1009]
	12	Volodimir G. Vovk, Aggregating strategies, Proceedings of the third annual workshop on Computational learning theory, p.371-386, August 06-08, 1990, Rochester, New York, United States
	13	V. G. Vovk, Universal forecasting algorithms, Information and Computation, v.96 n.2, p.245-277, Feb. 1992 [doi>10.1016/0890-5401(92)90050-P]
	14	V. G. Vovk, A game of prediction with expert advice, Proceedings of the eighth annual conference on Computational learning theory, p.51-60, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225304]
	15	V. Vovk, Derandomizing stochastic prediction strategies, Proceedings of the tenth annual conference on Computational learning theory, p.32-44, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267473]
	16	V. G. Vovk, Probability Theory for the Brier Game, Proceedings of the 8th International Conference on Algorithmic Learning Theory, p.323-338, October 06-08, 1997
	17	A. K. Zvonkin and L. A. Levin. The complexity of finite objects and the development of the concepts of information and randomness by means of the theory of algorithms. Russian Math. Surveys, 25:83-124, 1970.

CITED BY 14

	V. V. V'yugin, Does snooping help?, Theoretical Computer Science, v.276 n.1-2, p.407-415, April 6, 2002
	Michael V. Vyugin , Vladimir V. V'yugin, Predictive complexity and information, Journal of Computer and System Sciences, v.70 n.4, p.539-554, June 2005
	V. V. V'yugin, Most sequences are stochastic, Information and Computation, v.169 n.2, p.252-263, September 15, 2001
	Yuri Kalnishkan , Vladimir Vovk , Michael V. Vyugin, How many strings are easy to predict?, Information and Computation, v.201 n.1, p.55-71, 25 August 2005
	Michael V. Vyugin , Vladimir V. V'yugin, On complexity of easy predictable sequences, Information and Computation, v.178 n.1, p.241-252, October 10, 2002
	V. V. V'yugin, Suboptimal measures of predictive complexity for absolute loss function, Information and Computation, v.175 n.2, p.146-157, June 15, 2002
	Yuri Kalnishkan, Linear relations between square-loss and Kolmogorov complexity, Proceedings of the twelfth annual conference on Computational learning theory, p.226-232, July 07-09, 1999, Santa Cruz, California, United States
	Yuri Kalnishkan , Volodya Vovk , Michael V. Vyugin, Loss functions, complexities, and the legendre transformation, Theoretical Computer Science, v.313 n.2, p.195-207, 17 February 2004
	Zinovi Rabinovich , Jeffrey S. Rosenschein, On the response of EMT-based control to interacting targets and models, Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, May 08-12, 2006, Hakodate, Japan
	Yuri Kalnishkan, General linear relations between different types of predictive complexity, Theoretical Computer Science, v.271 n.1-2, p.181-200, January 28, 2002
	Adam Kalai , Santosh Vempala, Efficient algorithms for universal portfolios, The Journal of Machine Learning Research, 3, 3/1/2003
	Alex Gammerman , Yuri Kalnishkan , Vladimir Vovk, On-line prediction with kernels and the complexity approximation principle, Proceedings of the 20th conference on Uncertainty in artificial intelligence, p.170-176, July 07-11, 2004, Banff, Canada
	V. Vovk, Derandomizing Stochastic Prediction Strategies, Machine Learning, v.35 n.3, p.247-282, June 1999
	Peter DeMarzo , Ilan Kremer , Yishay Mansour, Online trading algorithms and robust option pricing, Proceedings of the thirty-eighth annual ACM symposium on Theory of computing, May 21-23, 2006, Seattle, WA, USA