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  Publications - by Co-Author

• Anagnostou • Assayag • Bachrach • Bar-Joseph • Baram • Begleiter • Bekkerman • Ben-David • Ben-Or • Birkland • Borodin • Brook • Chang • Chou • Cooperstock • Derbeko • Dubnov • Etzion-Rosenberg • Fiat • Fine • Gdalyahu • Gerzon • Gogan • Hadzilacos • Isler • Kaniel • Karp • Kimmel • Kleinberg • Klugerman • Leighton • Linial • Lischinski • Luz • Mccallum • Meir • Nevo • Nisenson • Pechyony • Peleg • Reinstaedtler • Schneidman • Seiden • Shakhnarovich • Sharon • Souroujon • Tishby • Turpin • Werman • Winter • Yariv • Yaroshinsky • Yom-Tov • Yona •

Anagnostou

  • E. Anagnostou, R. El-Yaniv and V. Hadzilacos. Memory Adaptive Self-Stabilizing Protocols. In Proceedings of the 6th International Workshop on Distributed Algorithms, pp. 203–220, Springer-Verlag, 1992.
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  • E. Anagnostou and R. El-Yaniv. More on the Power of Random Walks: Uniform Self-Stabilizing Randomized Algorithms. In Proceedings of the 5th International Workshop on Distributed Algorithms, pp. 31–51, Springer-Verlag, 1991.
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Assayag

  • S. Dubnov, G. Assayag and R. El-Yaniv. Universal classification applied to musical sequences. In Proceedings of the 1998 International Computer Music Conference (ICMC), pp. 332–340, 1998.
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Bachrach

  • R. Bachrach, R. El-Yaniv and M. Reinstaedtler. On the Competitive Theory and Practice of List Accessing Algorithms. Algorithmica, 32:201–246, 2002.
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  • R. Bachrach and R. El-Yaniv. Online list Accessing Algorithms and Their Applications: Recent Empirical Evidence. In Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms (SODA), pp. 53–62, 1997.
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Bar-Joseph

Baram

  • Y. Baram, R. El-Yaniv and K. Luz. Online Choice of Active Learning Algorithms. Journal of Machine Learning Research, 5:255–291, March 2004.
    Download:      
  • Y. Baram, R. El-Yaniv and K. Luz. Online Choice of Active Learning Algorithms. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), pp. 19–26, 2003.
    Download:  
  • G. Shakhnarovich, R. El-Yaniv and Y. Baram. Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML), pp. 521–528, 2001.
    Download:      

Begleiter

  • R. Begleiter and R. El-Yaniv. Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition. Journal of Machine Learning Research, 7:379–411, 2006.
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  • R. Begleiter and R. El-Yaniv. Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition. Technical Report CS-2005-13, Technion - Israel Institute of Technology, 2005.
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  • R. Begleiter, R. El-Yaniv and G. Yona. On Prediction Using Variable Order Markov Models. Journal of Artificial Intelligence Research (JAIR), 22:385–421, 2004.
    Download:      

Bekkerman

  • R. Bekkerman, R. El-Yaniv and A. McCallum. Multi-Way Distributional Clustering via Pairwise Interactions. In Proceedings of the 22nd International Conference on Machine Learning (ICML), pp. 41–48, 2005.
    Download:      
  • R. Bekkerman, R. El-Yaniv, N. Tishby and Y. Winter. Distributional Word Clusters vs. Words for Text Categorization. Journal of Machine Learning Research, 3:1183–1208, 2003.
    Download:      
  • R. Bekkerman, R. El-Yaniv, N. Tishby and Y. Winter. On feature distributional clustering for text categorization. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 146–153, 2001.
    Download:      

Ben-David

  • R. Meir, R. El-Yaniv and S. Ben-David. Localized Boosting. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT), pp. 190–199, 2000.
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Ben-Or

  • M. Ben-Or and R. El-Yaniv. Optimally-Resilient Interactive Consistency in Constant Time. Distributed Computing, 4(16):249–262, 2003.
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Birkland

  • I. Sharon, A. Birkland, K. Chang, R. El-Yaniv and G. Yona. Correcting BLAST E-values for Low-Complexity Segments. Journal of Computational Biology, 12(7):978–1001, 2005.
    Download:    

Borodin

  • A. Borodin, R. El-Yaniv and V. Gogan. Can We Learn to Beat the Best Stock. Journal of Artificial Intelligence Research, 21:579–594, May 2004.
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  • A. Borodin, R. El-Yaniv and V. Gogan. Can We Learn to Beat the Best Stock. In Advances in Neural Information Processing Systems (NIPS) 16, pp. 345–352, MIT Press, Cambridge, MA, 2003.
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  • A. Borodin, R. El-Yaniv and V. Gogan. On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). In Proceedings of the 4th Latin American Symposium on Theoretical Informatics (LATIN'00), pp. 173–196, 2000.
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  • A. Borodin and R. El-Yaniv. On Randomization in On-Line Computation. Information and Computation, 150(2):244–267, 1999.
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  • A. Borodin and R. El-Yaniv. Online Computation and Competitive Analysis, Cambridge University Press, 1998.
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  • A. Borodin and R. El-Yaniv. On Randomization in On-Line Computation. In Proceedings of the 12th Annual IEEE Conference on Computational Complexity (CCC '97), pp. 226–238, 1997.
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Brook

Chang

  • I. Sharon, A. Birkland, K. Chang, R. El-Yaniv and G. Yona. Correcting BLAST E-values for Low-Complexity Segments. Journal of Computational Biology, 12(7):978–1001, 2005.
    Download:    

Chou

  • A. Chou, J. Cooperstock, R. El-Yaniv, M. Klugerman and T. Leighton. The Statistical Adversary Allows Optimal Money-Making Trading Strategies. In Proceedings of the Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 467–476, 1995.
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Cooperstock

  • A. Chou, J. Cooperstock, R. El-Yaniv, M. Klugerman and T. Leighton. The Statistical Adversary Allows Optimal Money-Making Trading Strategies. In Proceedings of the Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 467–476, 1995.
    Download:      

Derbeko

  • P. Derbeko, R. El-Yaniv and R. Meir. Explicit Learning Curves for Transductive Learning and Application to Clustering and Compression Algorithms. Journal of Artificial Intelligence Research (JAIR), 22:117–142, 2004.
    Download:      
  • P. Derbeko, R. El-Yaniv and R. Meir. Error Bounds for Transductive Learning via Compression and Clustering. In Advances in Neural Information Processing Systems (NIPS) 16, pp. 1085–1092, MIT Press, Cambridge, MA, 2003.
    Download:      
  • P. Derbeko, R. El-Yaniv and R. Meir. Variance Optimized Bagging. In Proceedings of the 13th European Conference on Machine Learning (ECML), pp. 60–71, 2002.
    Download:      

Dubnov

Etzion-Rosenberg

  • R. El-Yaniv and N. Etzion-Rosenberg. Hierarchical Multiclass Decompositions with Application to Authorship Determination. Technical Report CS-2004-15, Technion - Israel Institute of Technology, 2004.
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Fiat

Fine

  • R. El-Yaniv, S. Fine and N. Tishby. Agnostic Classification of Markovian sequences. In Proceedings of Neural Information Processing Systems (NIPS), pp. 465–471, MIT Press, 1997.
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Gdalyahu

Gerzon

  • R. El-Yaniv and L. Gerzon. Effective Transductive Learning via Objective Model Selection. Pattern Recognition Letters, 26(13):2104–2115, 2005.
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  • R. El-Yaniv and L. Gerzon. Effective Transductive Learning via PAC-Bayesian Model Selection. Technical Report CS-2004-05, Technion - Israel Institute of Technology, 2004.
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Gogan

  • A. Borodin, R. El-Yaniv and V. Gogan. Can We Learn to Beat the Best Stock. Journal of Artificial Intelligence Research, 21:579–594, May 2004.
    Download:  
  • A. Borodin, R. El-Yaniv and V. Gogan. Can We Learn to Beat the Best Stock. In Advances in Neural Information Processing Systems (NIPS) 16, pp. 345–352, MIT Press, Cambridge, MA, 2003.
    Download:      
  • A. Borodin, R. El-Yaniv and V. Gogan. On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). In Proceedings of the 4th Latin American Symposium on Theoretical Informatics (LATIN'00), pp. 173–196, 2000.
    Download:    

Hadzilacos

  • E. Anagnostou, R. El-Yaniv and V. Hadzilacos. Memory Adaptive Self-Stabilizing Protocols. In Proceedings of the 6th International Workshop on Distributed Algorithms, pp. 203–220, Springer-Verlag, 1992.
    Download:  

Isler

Kaniel

Karp

  • R. El-Yaniv, A. Fiat, R.M. Karp and G. Turpin. Optimal Search and One-Way Trading Online Algorithms. Algorithmica, 30(1):101–139, 2001.
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  • R. El-Yaniv and R.M. Karp. Nearly Optimal Competitive Online Replacement Policies. Mathematics of Operations Research, 22(4):814–839, 1997.
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  • R. El-Yaniv and R.M. Karp. The Mortgage Problem. In 2nd Israel Symposium on Theory of Computing and Systems, pp. 304–312, 1993.
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  • R. El-Yaniv, A. Fiat, R.M. Karp and G. Turpin. Competitive Analysis of Financial Games. In IEEE Symposium on Foundations of Computer Science (FOCS), pp. 327–333, 1992.
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Kimmel

Kleinberg

  • R. El-Yaniv and J. Kleinberg. Geometric Two-Server Algorithms. Information Processing Letters, 53(6):355–358, 1995.
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Klugerman

  • A. Chou, J. Cooperstock, R. El-Yaniv, M. Klugerman and T. Leighton. The Statistical Adversary Allows Optimal Money-Making Trading Strategies. In Proceedings of the Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 467–476, 1995.
    Download:      

Leighton

  • A. Chou, J. Cooperstock, R. El-Yaniv, M. Klugerman and T. Leighton. The Statistical Adversary Allows Optimal Money-Making Trading Strategies. In Proceedings of the Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 467–476, 1995.
    Download:      

Linial

Lischinski

Luz

  • Y. Baram, R. El-Yaniv and K. Luz. Online Choice of Active Learning Algorithms. Journal of Machine Learning Research, 5:255–291, March 2004.
    Download:      
  • Y. Baram, R. El-Yaniv and K. Luz. Online Choice of Active Learning Algorithms. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), pp. 19–26, 2003.
    Download:  

Mccallum

  • R. Bekkerman, R. El-Yaniv and A. McCallum. Multi-Way Distributional Clustering via Pairwise Interactions. In Proceedings of the 22nd International Conference on Machine Learning (ICML), pp. 41–48, 2005.
    Download:      

Meir

  • A. Brook, R. El-Yaniv, E. Isler, R. Kimmel, R. Meir and D. Peleg. Breast Cancer Diagnosis From Biopsy Images Using Generic Features and SVMs. Technion - Israel Institute of Technology, 2006.
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  • P. Derbeko, R. El-Yaniv and R. Meir. Explicit Learning Curves for Transductive Learning and Application to Clustering and Compression Algorithms. Journal of Artificial Intelligence Research (JAIR), 22:117–142, 2004.
    Download:      
  • P. Derbeko, R. El-Yaniv and R. Meir. Error Bounds for Transductive Learning via Compression and Clustering. In Advances in Neural Information Processing Systems (NIPS) 16, pp. 1085–1092, MIT Press, Cambridge, MA, 2003.
    Download:      
  • M. Nisenson, I. Yariv, R. El-Yaniv and R. Meir. Towards Behaviometric Security Systems: Learning to Identify a Typist. In Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 363–374, 2003.
    Download:  
  • P. Derbeko, R. El-Yaniv and R. Meir. Variance Optimized Bagging. In Proceedings of the 13th European Conference on Machine Learning (ECML), pp. 60–71, 2002.
    Download:      
  • R. Meir, R. El-Yaniv and S. Ben-David. Localized Boosting. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT), pp. 190–199, 2000.
    Download:    

Nevo

  • Z. Nevo and R. El-Yaniv. On Online Learning of Decision Lists. Journal of Machine Learning Research (JMLR), 3:271–301, October 2002.
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Nisenson

  • R. El-Yaniv and M. Nisenson. Optimal Single-Class Classification Strategies. In Advances in Neural Information Processing Systems (NIPS) 20, MIT Press, Cambridge, MA, 2006.
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  • M. Nisenson, I. Yariv, R. El-Yaniv and R. Meir. Towards Behaviometric Security Systems: Learning to Identify a Typist. In Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 363–374, 2003.
    Download:  

Pechyony

  • R. El-Yaniv, D. Pechyony and E. Yom-Tov. Superior Multiclass Classification Through Margin-Optimized Single Binary Problem. Technical Report H-0243, IBM Research Report, 2006.
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  • R. El-Yaniv and D. Pechyony. Stable Transductive Learning.. In Proceedings of the 19th Annual Conference on Computational Learning Theory (COLT), pp. 35–49, 2006.
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Peleg

Reinstaedtler

  • R. Bachrach, R. El-Yaniv and M. Reinstaedtler. On the Competitive Theory and Practice of List Accessing Algorithms. Algorithmica, 32:201–246, 2002.
    Download:      

Schneidman

Seiden

  • R. Yaroshinsky, R. El-Yaniv and S. Seiden. How to Better Use Expert Advice. Machine Learning, 55(3):271–309, June 2004.
    Download:      

Shakhnarovich

  • G. Shakhnarovich, R. El-Yaniv and Y. Baram. Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML), pp. 521–528, 2001.
    Download:      

Sharon

  • I. Sharon, A. Birkland, K. Chang, R. El-Yaniv and G. Yona. Correcting BLAST E-values for Low-Complexity Segments. Journal of Computational Biology, 12(7):978–1001, 2005.
    Download:    

Souroujon

  • R. El-Yaniv and O. Souroujon. Iterative Double Clustering for Unsupervised and Semi-Supervised Learning. In Advances in Neural Information Processing Systems (NIPS) 14, pp. 1025–1032, MIT Press, Cambridge, MA, 2001.
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Tishby

  • R. Bekkerman, R. El-Yaniv, N. Tishby and Y. Winter. Distributional Word Clusters vs. Words for Text Categorization. Journal of Machine Learning Research, 3:1183–1208, 2003.
    Download:      
  • S. Dubnov, R. El-Yaniv, Y. Gdalyahu, E. Schneidman, N. Tishby and G. Yona. A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles. Machine Learning, 47(1):35–61, 2002.
    Download:    
  • R. Bekkerman, R. El-Yaniv, N. Tishby and Y. Winter. On feature distributional clustering for text categorization. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 146–153, 2001.
    Download:      
  • R. El-Yaniv, S. Fine and N. Tishby. Agnostic Classification of Markovian sequences. In Proceedings of Neural Information Processing Systems (NIPS), pp. 465–471, MIT Press, 1997.
    Download:    

Turpin

Werman

Winter

  • R. Bekkerman, R. El-Yaniv, N. Tishby and Y. Winter. Distributional Word Clusters vs. Words for Text Categorization. Journal of Machine Learning Research, 3:1183–1208, 2003.
    Download:      
  • R. Bekkerman, R. El-Yaniv, N. Tishby and Y. Winter. On feature distributional clustering for text categorization. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 146–153, 2001.
    Download:      

Yariv

  • M. Nisenson, I. Yariv, R. El-Yaniv and R. Meir. Towards Behaviometric Security Systems: Learning to Identify a Typist. In Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 363–374, 2003.
    Download:  

Yaroshinsky

  • R. Yaroshinsky, R. El-Yaniv and S. Seiden. How to Better Use Expert Advice. Machine Learning, 55(3):271–309, June 2004.
    Download:      

Yom-Tov

  • R. El-Yaniv, D. Pechyony and E. Yom-Tov. Superior Multiclass Classification Through Margin-Optimized Single Binary Problem. Technical Report H-0243, IBM Research Report, 2006.
    Download:  

Yona

  • I. Sharon, A. Birkland, K. Chang, R. El-Yaniv and G. Yona. Correcting BLAST E-values for Low-Complexity Segments. Journal of Computational Biology, 12(7):978–1001, 2005.
    Download:    
  • R. Begleiter, R. El-Yaniv and G. Yona. On Prediction Using Variable Order Markov Models. Journal of Artificial Intelligence Research (JAIR), 22:385–421, 2004.
    Download:      
  • S. Dubnov, R. El-Yaniv, Y. Gdalyahu, E. Schneidman, N. Tishby and G. Yona. A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles. Machine Learning, 47(1):35–61, 2002.
    Download:    

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