List of Publications

Refereed journal papers‡
  1. J. Ratsaby. Bounded complexity approximation of fractal sets, Journal of Computational Dynamics, doi: 10.3934/jcd.2024033. .
  2. J. Ratsaby. Fractal oracle numbers, Fractals—Complex Geometry, Patterns, and Scaling in Nature and Society, vol. 32(1), Article No. 2450029, 2024.
  3. J. Ratsaby. Learning half-spaces on general infinite spaces equipped with a distance function, Information and Computation, Vol. 291, Article No. 105008,2023.
  4. M. Anthony, J. Ratsaby. Large-width (LW) machine learning algorithm, Progress in Artificial Intelligence , Vol. 9, pp.275-285, 2020.
  5. A. Belousov, J. Ratsaby. A Parallel Computation Algorithm for Image Feature Extraction, Journal of Advances in Applied and Computational Mathematics, Vol. 6, pp. 1-18, 2019.
  6. J. Ratsaby. On deterministic finite state machines in random environments, Probability in the Engineering and Informational Sciences, Vol. 33(4), pp. 528-563, 2019.
  7. M. Anthony, J. Ratsaby. Large width nearest prototype classification on general distance spaces, Theoretical Computer Science, Vol. 738, pp. 65-79, 2018.
  8. M. Anthony, J. Ratsaby. Large-width bounds for learning half-spaces on distance spaces, Discrete Applied Mathematics, vol. 243, pp. 73-89, 2018.
  9. M. Anthony, J. Ratsaby. Classification based on prototypes with spheres of influence, Information and Computation, vol. 256, pp. 372-380, 2017.
  10. M. Anthony, J. Ratsaby. Multi-category classifiers and sample width, Journal of Computer and System Sciences, vol. 82(8), pp. 1223-1231, 2016.
  11. M. Anthony, J. Ratsaby. A probabilistic approach to case-based inference, Theoretical Computer Science, vol. 589, pp. 61-75, 2015.
  12. M. Anthony, J. Ratsaby. Learning bounds via sample width for classifiers on finite metric spaces, Theoretical Computer Science, Vol. 529, Pages 2-10, 2014.
  13. M. Anthony and J. Ratsaby. A hybrid classifier based on boxes and nearest neighbors, Discrete Applied Mathematics, Vol. 529, Pages 2-10, 2014.
  14. L. Kovacs, J. Ratsaby. Analysis of linear interpolation of fuzzy sets with entropy-based distances, Acta Polytechnica Hungarica, 10(3), pp. 51-64, 2013.
  15. M. Anthony, J. Ratsaby. Analysis of a multi-category classifier, Discrete Applied Mathematics, 160(16), pp. 2329-2338, 2012.
  16. M. Anthony, J. Ratsaby. Robust cutpoints in the logical analysis of numerical data, Discrete Applied Mathematics, 160(4), pp. 355-364, 2012.
  17. J. Ratsaby. On the descriptional complexity of systems and their output response, Mathematics in Engineering, Science and Aerospace, 2(3), pp. 287-298, 2011.
  18. J. Ratsaby. An empirical study of the complexity and randomness of prediction error sequences, Communications in Nonlinear Science and Numerical Simulations, 16, pp. 2832-2844, 2011.
  19. J. Ratsaby. On the sysRatio and its critical point, Mathematical and Computer Modeling, 53, pp. 939-944, 2011.
  20. J. Ratsaby. Some consequences of the complexity of intelligent prediction, Broad Research in Artificial Intelligence and Neuroscience, Special Issue on Complexity in Sciences and Artificial Intelligence, 1(3), pp. 113-118, 2010.
  21. M. Anthony, J. Ratsaby. Maximal width learning of binary functions, Theoretical Computer Science, 411, pp. 138-147, 2010.
  22. J. Ratsaby, J. Chaskalovic. On the algorithmic complexity of static structures, Journal of Systems Science and Complexity, 23(6), pp. 1037-1053, 2010.
  23. J. Chaskalovic, J. Ratsaby. Interaction of a self vibrating beam with chaotic external forces, Comptes Rendus Mecanique, 338(1), pp. 33-39, 2010.
  24. J. Ratsaby. Estimate of the number of restricted integer-partitions, Applicable Analysis and Discrete Mathematics, 2(2): 222-233, 2008
  25. J. Ratsaby. On the complexity of binary samples, Annals of Mathematics and Artificial Intelligence, 52, pp. 55-65, 2008
  26. J. Ratsaby. An Algorithmic Complexity Interpretation of Lin's Third Law of Information Theory, Entropy, 10(1), pp. 6-14, 2008
  27. B. Ycart, J. Ratsaby. VC and related dimensions of random function classes, Discrete Mathematics and Theoretical Computer Science, 10(1),pp. 113-128, 2008
  28. J. Ratsaby. Constrained versions of Sauer's lemma, Discrete Applied Mathematics, 156(14), pp. 2753-2767, 2008
  29. J. Ratsaby. On the complexity of constrained VC-classes, Discrete Applied Mathematics, 156(6), pp. 903-910, ,2008
  30. J. Ratsaby. Density of smooth Boolean functions, Applicable Analysis and Discrete Mathematics, 1(1), pp. 184-198, 2007
  31. J. Ratsaby. On the VC-dimension and Boolean functions with long runs. Journal of Discrete Mathematical Sciences and Cryptography, 10(2), pp. 205-225, 2007
  32. B. Ycart, J. Ratsaby. The VC-dimension of k-uniform random hypergraphs, Random Structures and Algorithms, 30, pp. 564-572, 2007
  33. J. Ratsaby. Complexity of hyperconcepts. Theoretical Computer Science, 363(1), pp. 2-10, 2006
  34. J. Ratsaby. On learning multicategory classification with sample queries. Information and Computation, 185(2), pp. 298-327, 2003
  35. V. Maiorov, J. Ratsaby. On the degree of approximation by manifolds of finite pseudo-dimension. Constructive Approximation, 15(2), pp. 291-300, 1999
  36. J. Ratsaby, V. Maiorov. On the learnability of rich function classes. Journal of Computer and System Sciences, 58(1), pp. 183-192, 1999
  37. V. Maiorov, R. Meir, and J. Ratsaby. On the approximation of functional classes equipped with a uniform measure using ridge functions. Journal of Approximation Theory, 99(1), pp. 95-111, 1999
  38. J. Ratsaby. Incremental learning with sample queries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 883-888, 1998
  39. V. Maiorov, J. Ratsaby, The degree of approximation of sets in Euclidean space using sets with bounded Vapnik-Chervonenkis dimension. Discrete Applied Mathematics, 86(1), pp. 81-93, 1998
  40. J. Ratsaby, V. Maiorov. On the value of partial information for learning by examples. Journal of Complexity, 13, pp. 509-544, 1998
Chapters in books
  1. J. Ratsaby. Combinatorial information distance , Advanced computational technologies, C. Enachescu, F. Gheorghe Filip, B. Iantovics (Eds.). - Bucuresti: Editura Academiei Romane (Romanian Academy Publishing House), pp. 201- 207, ISBN 978-973-27-2256-5, 2012
  2. J. Ratsaby. Valiant's PAC Model of learning, In Ming-Yang Kao (Ed.), Encyclopedia of Algorithms, pp..622-624, ISBN 978-0-387-30770-1, Springer, (invited chapter), 2008
Refereed papers in conference proceedings
  1. J. Ratsaby, A. Timashkov, Multi-GPU processing of unstructured data for machine learning, Proc. of the 39th International Conference on High Performance Computing (ISC’2024), Hamburg, Germany, May 12–16, 2024
  2. J. Ratsaby, A. Timashkov, Accelerating the LZ-complexity algorithm, Proc. of 29th International Conference on Parallel and Distributed Systems (ICPADS’23), Ocean Flower Island, Hainan, China, December 17-21, 2023
  3. J. Ratsaby, A, Sabaty, Parallelizing the Large-Width learning algorithm, Proc. of International Conference on the Science of Electrical Engineering (ICSEE'2018),Eilat, ISRAEL, Dec. 12-14, 2018
  4. J. Ratsaby, On how complexity affects the stability of a predictor, in A. Storkey, F. Perez-Cruz (Eds.), Proc. of Machine Learning Research, Vol. 84, pp. 161-167, Artificial Intelligence and Statistics (AISTATS-2018), Playa Blanca, Lanzarote, Canary Islands, April 9-11, 2018
  5. J. Ratsaby, On the errors of a predictor which is calibrated to its random environment, in A. Lapidoth, S. M. Moser (Eds.), Proc. of the International Zurich Seminar on Information and Communications (IZS 2018), ETH-Zurich, pp. 162-166, Feb. 21-23, 2018
  6. A. Belousov, J. Ratsaby, A parallel distributed processing algorithm for image feature extraction, in E. Fromont, T. De Bie, M. van Leeuwen (Eds.), Advances in Intelligent Data Analysis XIV, Proc. of the 14th International Symposium on Intelligent Data Analysis (IDA'15), Springer LNCS Vol. 9385, Saint-Etienne, France, Oct. 22-24, 2015
  7. J. Ratsaby, On complexity and randomness of Markov-chain prediction, Proc. of IEEE Information Theory Workshop (ITW '15), Jerusalem, Israel, April 26 - May 1, 2015
  8. A. Belousov, J. Ratsaby. Massively Parallel Computations of the LZ-complexity of Strings, Proc. of the 28th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'14), pp. 1-5, Eilat, Israel, Dec. 3-5, 2014
  9. L. Kovacs, J. Ratsaby, A New Pseudo-Metric for Fuzzy Sets, in L. Rutkowski et al. (Eds.), Proc. of Artificial Intelligence and Soft Computing - 13th International Conference (ICAISC 2014), Part I, Springer LNCS 8467, pp. 205-216, Zakopane, Poland, June 1-5, 2014
  10. M. Anthony, J. Ratsaby. Maximal-margin case-based inference, Proc. of 13th UK Workshop on Computational Intelligence, (UKCI-2013), Sept. 9-11, 2013
  11. U. Chester, J. Ratsaby. Image classification and clustering using a universal distance measure, in N. Brisaboa, O. Pedreira, and P. Zezula (Eds.), Proc. of the 6th Int'l conf. on Similarity Search and Applications (SISAP 2013), Springer LNCS 8199, pp. 59-72, La Coruna, Spain, Oct. 2-4, 2013
  12. M. Anthony, J. Ratsaby. Quantifying accuracy of learning via sample width, Proc. of the IEEE Symposium on Foundations of Computational Intelligence (FOCI'13), pp. 1-7, Singapore, April 16-19, 2013
  13. J. Ratsaby, V. Sirota. FPGA-based data compressor based on Prediction by Partial Matching, Proc. of the 27th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'12), pp. 1-5, Eilat, Israel, Nov. 14-17, 2012
  14. G. Kaspi, J. Ratsaby. Parallel Processing Algorithm for Bayesian Network Inference, Proc. of the 27th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'12), Nov. pp. 1-5, Eilat, Israel, Nov. 14-17, 2012
  15. U. Chester, J. Ratsaby. Universal distance measure for images, Proc. of the 27th IEEE Convention of Electrical and El­ectronics Engineers in Israel (IEEEI'12), pp. 1-4, Eilat, Israel, Nov. 14-17, 2012, 2012
  16. J. Ratsaby. Information Set-Distance, Proc. of the 2010 Mini-Conference on Applied Theoretical Computer Science (MATCOS-2010), pp. 61-64, University of Primorska Press, Koper, Slovenia, Oct. 13-14, 2011
  17. J. Ratsaby. Prediction by Compression, Proc. of Eighth IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPRA 2011), pp. 282-288, Insbruck, Austria, Feb. 16-18, 2011
  18. J. Ratsaby, D. Zavielov. An FPGA-based pattern classifier using data compression, Proc. of the 26th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI 2010), pp. 320-324, Eilat, Israel, Nov. 17-20, 2010
  19. J. Ratsaby. On the relation between a system's complexity and its interaction with random environments, Proceedings of International symposium on stochastic models in reliability engineering, life sciences and operations management (SMRLO'10), p.893-901, Sami Shamoon College of Engineering, Be'er Sheva, Israel, February 8-11, 2010
  20. J. Ratsaby. On the randomness in learning. Proceedings of the 7th IEEE International Conference on Computational Cybernetics (ICCC'09), pp. 141-145, Palma de Mallorca, Spain, Nov. 26 - 29, 2009
  21. J. Ratsaby, J. Chaskalovic. Random patterns and complexity in static structures, in D.A. Karras et. al. (Eds.), Proceedings of International Conference on Artificial Intelligence and Pattern Recognition (AIPR'09), pp. 255-261, Orlando, Florida, USA, July 13-16, 2009
  22. J. Ratsaby. Information efficiency, In J. van Leeuwen et al. (Eds.): Proceedings of 33rd International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM'07), Springer LNCS Vol. 4362, pp. 475-487, Harrachov, Czech Republic, Jan. 20 - 26, 2007
  23. J. Ratsaby. On the combinatorial representation of information, In D.Z. Chen and D.T. Lee (Eds.) Proceedings of Twelfth International Computing and Combinatorics Conference (COCOON'06), Springer-Verlag LNCS 4112, pp. 479-488, Taipei, Taiwan,, August 15-18, 2006
  24. J. Ratsaby. Complexity of VC-dimension classes of sequences with long repetitive runs, In J. F. Michon, P. Valarcher and J. B. Yunes (Eds.), Proceedings of Second International Workshop Boolean Functions: Cryptography Applications (BFCA'06), pp. 13-28, Presses Universitaires de Rouen et du Havre, Rouen, France, March 13-15, 2006
  25. J. Ratsaby. On the complexity of samples for learning. In K.Y. Chwa, J. I. Munro (Eds.): Proceedings of Tenth International Computing and Combinatorics Conference (COCOON'04), Springer LNCS vol. 3106, pp. 198-209, Jeju Island, Korea, August 17-20, 2004
  26. J. Ratsaby. A stochastic gradient descent algorithm for structural risk minimization. In K. P. Jantke R. Gavald`a and E. Takimoto, editors, Proceedings of 14th International Conference Algorithmic Learning Theory (ALT'04), Springer LNAI vol. 2842, pp. 205-220, Sapporo, Japan, October 17-19, 2003
  27. J. Ratsaby, S.S. Venkatesh. On partially blind learning complexity. In Proc. of IEEE International Symposium on Circuits and Systems (ISCAS'08), pp. II-765-768, Geneva, Switzerland, May 28-31, 2000
  28. J. Ratsaby. An incremental nearest neighbor algorithm with queries. In M. I. Jordan, M. J. Kearns, and S. A. Solla, (Eds.), Advances in Neural Information Processing Systems, (NIPS '98), Vol. 10, pp. 612- 619, MIT Press, 1998
  29. J. Ratsaby, V. Maiorov. Generalization of the PAC-model for incomplete side information. In S. Ben-David, editor, Proceedings 3rd European Conference on Computational Learning Theory (ECOLT'97), Springer LNCS 1208, pp. 51-65, Jerusalem, Israel, March 17-19, 1997
  30. J. Ratsaby, R. Meir, V. Maiorov. Towards robust model selection using estimation and approximation error bounds. In A. Blum, M. Kearns (Eds.): Proc. of Ninth Annual Conference on Computational Learning Theory (COLT'96), pp. 57- 67, Desenzano del Garda, Italy, June 28 - July 1, 1996
  31. J. Ratsaby, S. S. Venkatesh. Learning from a mixture of labeled and unlabeled examples with parametric side information. In W. Maass (Ed.), Proceedings 8th Annual Conference on Computational Learning Theory (COLT'95), pp. 412- 417, Santa Cruz, California, USA, July 5-8, 1995
  32. J. Ratsaby, S. S. Venkatesh. Learning from a mixture of labeled and unlabeled examples. Proceedings of 33rd Allerton Conference on Communication, Control, and Computing, pp. 1002-1009, Allerton, Illinois, USA, October 4-6, 1995
Refereed papers presented at scientific conferences (no proceedings)
  1. A. Etinger, B. Kapilevich, B. Litvak, J. Ratsaby. Classification of mm–wave images obtained from noise–illuminated targets, International Congress of Imaging Science (ICIS'2014), Tel Aviv, Israel, May 12–14, 2014
  2. M. Anthony and J. Ratsaby. The performance of a new hybrid classifier based on boxes and nearest neighbors, International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, FL., USA, January 9-11, 2012
  3. J. Ratsaby. Randomness properties of statistical prediction. The 55th meeting of the Israel physics society (IPS'09), Bar Ilan University, Ramat-Gan, Israel, Dec. 13, 2009
  4. J. Ratsaby. Some consequences of the complexity of intelligent prediction, International symposium on understanding intelligent and complex systems (UICS'09), Petru Maior University of Targu-Mures, Romania, Oct. 22-23, 2009
  5. J. Ratsaby. A distance measure for properties of Boolean functions. Workshop on Boolean Functions: Theory, Algorithms and Application, In Memory of Peter L. Hammer, CRI, University of Haifa, Haifa, January 27 - February 1, 2008
  6. J. Ratsaby. Density of Smooth Boolean Functions, International Mathematical Conference - Topics in Mathematical Analysis and Graph Theory (MAGT'06), University of Belgrade, Belgrade, Sept. 1-4, 2006
  7. J. Ratsaby. Complexity of constrained VC-Classes. International Scientific Annual Conference on Operations Research, Bremen, Germany, September 7-9, 2005
  8. J. Ratsaby. A Sharp Threshold Result for VC- Classes of Large-Margin Functions, EU PASCAL Workshop on Learning Theoretic and Bayesian Inductive Principles, Gatsby Computational Neuroscience Unit, University College London, London, July 19-21, 2004
  9. J. Ratsaby. Meeting the challenges of e-business by distributed artificial intelligence, Bar-Ilan International Symposium on the Foundations of Artificial Intelligence, Honoring: Yaacov Choueka, Ramat-Gan, Israel, June 25-27, 2001
  10. J. Ratsaby, G. Barnea. Automatic distributed intelligence: Merging distributed computing with machine learning for internet-based intelligent applications, International Joint Conference of Artificial Intelligence (IJCAI'99), Workshop on Learning about Users, Stockholm, Sweden, July 31, 1999
  11. J. Ratsaby, S. S. Venkatesh. Learning classification with few labeled examples, Advances in Neural Information Processing Systems (NIPS'92), Session II on Complexity, Learning and Generalization, Denver/Vail, Colorado, USA, Nov. 30 - Dec. 3, 1992
Dissertation
  1. J. Ratsaby. The Complexity of Learning from a Mixture of Labeled and Unlabeled Examples , Ph.D. Dissertation, University of Pennsylvania, April Abstract, 1994

‡ Authors in alphabetical order by last names in papers: 1–20, 42–67. In the remaining papers, the first author is the corresponding author.