List of Publications

Refereed papers
  1. J. Ratsaby. Compression ratio of fractal sets, Fractals—Complex Geometry, Patterns, and Scaling in Nature and Society, Vol 34(3), Article No. 2650001, https://doi.org/10.1142/S0218348X26500015, 2026
  2. J. Ratsaby. Fractal information density. Chaos, Solitons and Fractals, Vol. 192, 115989, 2025
  3. J. Ratsaby. Bounded complexity approximation of fractal sets, Journal of Computational Dynamics, Vol. 12(2), pp. 281-304, 2025
  4. J. Ratsaby. On system complexity, stability and performance: application to prediction, Mathematics and Mechanics of Complex Systems, Vol. 12(4), pp. 411–470, DOI 10.2140/memocs.2024.12.411 2024
  5. J. Ratsaby. Fractal oracle numbers, Fractals—Complex Geometry, Patterns, and Scaling in Nature and Society, vol. 32(1), Article No. 2450029, 2024
  6. 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
  7. J. Ratsaby. Learning half-spaces on general infinite spaces equipped with a distance function, Information and Computation, Vol. 291, Article No. 105008,2023
  8. 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
  9. M. Anthony, J. Ratsaby. Large-width (LW) machine learning algorithm, Progress in Artificial Intelligence , Vol. 9, pp.275-285, 2020 (Implementated as a WEKA package)
  10. 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
  11. J. Ratsaby. On deterministic finite state machines in random environments, Probability in the Engineering and Informational Sciences, Vol. 33(4), pp. 528-563, 2019.
  12. M. Anthony, J. Ratsaby. Large width nearest prototype classification on general distance spaces, Theoretical Computer Science, Vol. 738, pp. 65-79, 2018
  13. M. Anthony, J. Ratsaby. Large-width bounds for learning half-spaces on distance spaces, Discrete Applied Mathematics, vol. 243, pp. 73-89, 2018
  14. 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
  15. 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
  16. 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
  17. M. Anthony, J. Ratsaby. Classification based on prototypes with spheres of influence, Information and Computation, vol. 256, pp. 372-380, 2017
  18. M. Anthony, J. Ratsaby. Multi-category classifiers and sample width, Journal of Computer and System Sciences, vol. 82(8), pp. 1223-1231, 2016
  19. M. Anthony, J. Ratsaby. A probabilistic approach to case-based inference, Theoretical Computer Science, vol. 589, pp. 61-75, 2015
  20. 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
  21. 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
  22. M. Anthony, J. Ratsaby. Learning bounds via sample width for classifiers on finite metric spaces, Theoretical Computer Science, Vol. 529, Pages 2-10, 2014
  23. M. Anthony and J. Ratsaby. A hybrid classifier based on boxes and nearest neighbors, Discrete Applied Mathematics, Vol. 529, Pages 2-10, 2014
  24. 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
  25. 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
  26. L. Kovacs, J. Ratsaby. Analysis of linear interpolation of fuzzy sets with entropy-based distances, Acta Polytechnica Hungarica, 10(3), pp. 51-64, 2013
  27. M. Anthony, J. Ratsaby. Maximal-margin case-based inference, Proc. of 13th UK Workshop on Computational Intelligence, (UKCI-2013), Sept. 9-11, 2013
  28. 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
  29. 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
  30. M. Anthony, J. Ratsaby. Analysis of a multi-category classifier, Discrete Applied Mathematics, 160(16), pp. 2329-2338, 2012
  31. M. Anthony, J. Ratsaby. Robust cutpoints in the logical analysis of numerical data, Discrete Applied Mathematics, 160(4), pp. 355-364, 2012
  32. 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
  33. 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
  34. 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
  35. 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
  36. J. Ratsaby. On the descriptional complexity of systems and their output response, Mathematics in Engineering, Science and Aerospace, 2(3), pp. 287-298, 2011
  37. 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
  38. J. Ratsaby. On the sysRatio and its critical point, Mathematical and Computer Modeling, 53, pp. 939-944, 2011
  39. 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
  40. 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
  41. 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
  42. M. Anthony, J. Ratsaby. Maximal width learning of binary functions, Theoretical Computer Science, 411, pp. 138-147, 2010
  43. J. Ratsaby, J. Chaskalovic. On the algorithmic complexity of static structures, Journal of Systems Science and Complexity, 23(6), pp. 1037-1053, 2010
  44. J. Chaskalovic, J. Ratsaby. Interaction of a self vibrating beam with chaotic external forces, Comptes Rendus Mecanique, 338(1), pp. 33-39, 2010
  45. 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
  46. 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
  47. 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
  48. 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
  49. J. Ratsaby. Estimate of the number of restricted integer-partitions, Applicable Analysis and Discrete Mathematics, 2(2): 222-233, 2008
  50. J. Ratsaby. On the complexity of binary samples, Annals of Mathematics and Artificial Intelligence, 52, pp. 55-65, 2008
  51. J. Ratsaby. An Algorithmic Complexity Interpretation of Lin's Third Law of Information Theory, Entropy, 10(1), pp. 6-14, 2008
  52. B. Ycart, J. Ratsaby. VC and related dimensions of random function classes, Discrete Mathematics and Theoretical Computer Science, 10(1),pp. 113-128, 2008
  53. J. Ratsaby. Constrained versions of Sauer's lemma, Discrete Applied Mathematics, 156(14), pp. 2753-2767, 2008
  54. J. Ratsaby. On the complexity of constrained VC-classes, Discrete Applied Mathematics, 156(6), pp. 903-910, ,2008
  55. 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
  56. J. Ratsaby. Density of smooth Boolean functions, Applicable Analysis and Discrete Mathematics, 1(1), pp. 184-198, 2007
  57. 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
  58. B. Ycart, J. Ratsaby. The VC-dimension of k-uniform random hypergraphs, Random Structures and Algorithms, 30, pp. 564-572, 2007
  59. 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
  60. J. Ratsaby. Complexity of hyperconcepts. Theoretical Computer Science, 363(1), pp. 2-10, 2006
  61. 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
  62. 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
  63. 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
  64. 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
  65. J. Ratsaby. On learning multicategory classification with sample queries. Information and Computation, 185(2), pp. 298-327, 2003
  66. J. Ratsaby, S.S. Venkatesh. On partially blind learning complexity. In Proc. of IEEE International Symposium on Circuits and Systems (ISCAS'00), pp. II-765-768, Geneva, Switzerland, May 28-31, 2000
  67. V. Maiorov, J. Ratsaby. On the degree of approximation by manifolds of finite pseudo-dimension. Constructive Approximation, 15(2), pp. 291-300, 1999
  68. J. Ratsaby, V. Maiorov. On the learnability of rich function classes. Journal of Computer and System Sciences, 58(1), pp. 183-192, 1999
  69. 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
  70. J. Ratsaby. Incremental learning with sample queries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 883-888, 1998
  71. 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
  72. J. Ratsaby, V. Maiorov. On the value of partial information for learning by examples. Journal of Complexity, 13, pp. 509-544, 1998
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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. J. Ratsaby. Complexity, Stability, and Robustness: A Unified Perspective on Predictive System Behavior. Conference on Modeling Complexity in Mechanics and Applied Mathematics: Theory, Experiments, and Simulations (MeMOCS'25), Castelnuovo Cilento, Salerno, Italy, September 21-26, 2025
  2. J. Ratsaby. Parallel computations for learning from unstructured data processing. Symposium on Parallel Computing and Applications, Ariel University, Israel, June 15, 2023
  3. J. Ratsaby. Bounded-complexity approximation of filled-Julia sets. Workshop on Geometry of Deterministic and Random Fractals Honouring the 60+1st birthday of Professor Károly Simon, Budapest University of Technology and Economics, June 27 - July 1, 2023
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. J. Ratsaby. Complexity of constrained VC-Classes. International Scientific Annual Conference on Operations Research, Bremen, Germany, September 7-9, 2005
  11. 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
  12. 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
  13. 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
  14. 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