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, DOI: 10.1142/S0218348X26500015, 2026
  2. J. Ratsaby. Fractal information density, Chaos, Solitons and Fractals, Vol. 192, Article No. 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, ISC High Performance 2024 Research Paper Proceedings (39th International Conference), pp. 1–8, Hamburg, Germany, May 12–16, 2024 (Slides)
  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 the 29th IEEE International Conference on Parallel and Distributed Systems (ICPADS'23), pp. 200–207, Ocean Flower Island, Hainan, China, Dec. 17–21, 2023 (Slides)
  9. M. Anthony, J. Ratsaby. Large-width machine learning algorithm, Progress in Artificial Intelligence, Vol. 9, pp. 275–285, 2020 (Implemented 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 the International Conference on the Science of Electrical Engineering (ICSEE'18), pp. 1–5, 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 the International Conference on Artificial Intelligence and Statistics (AISTATS'18), PMLR Vol. 84, pp. 161–167, Playa Blanca, Lanzarote, Canary Islands, Apr. 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'18), pp. 162–166, ETH Zürich, Zürich, Switzerland, 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. J. Ratsaby. Valiant's PAC model of learning, in Ming-Yang Kao (Ed.), Encyclopedia of Algorithms, pp. 1497–1500, Springer (invited chapter), 2016
  19. M. Anthony, J. Ratsaby. Multi-category classifiers and sample width, Journal of Computer and System Sciences, Vol. 82(8), pp. 1223–1231, 2016
  20. M. Anthony, J. Ratsaby. A probabilistic approach to case-based inference, Theoretical Computer Science, Vol. 589, pp. 61–75, 2015
  21. 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, pp. 61–71, Saint-Etienne, France, Oct. 22–24, 2015
  22. J. Ratsaby. On complexity and randomness of Markov-chain prediction, Proc. of the IEEE Information Theory Workshop (ITW'15), pp. 1–5, DOI: 10.1109/ITW.2015.7133078, Jerusalem, Israel, Apr. 26 – May 1, 2015
  23. M. Anthony, J. Ratsaby. Learning bounds via sample width for classifiers on finite metric spaces, Theoretical Computer Science, Vol. 529, pp. 2–10, 2014
  24. M. Anthony, J. Ratsaby. A hybrid classifier based on boxes and nearest neighbors, Discrete Applied Mathematics, Vol. 172, pp. 1–11, 2014
  25. 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
  26. L. Kovacs, J. Ratsaby. A new pseudo-metric for fuzzy sets, in L. Rutkowski et al. (Eds.), Proc. of the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC'14), Part I, Springer LNCS Vol. 8467, pp. 205–216, Zakopane, Poland, June 1–5, 2014
  27. L. Kovacs, J. Ratsaby. Analysis of linear interpolation of fuzzy sets with entropy-based distances, Acta Polytechnica Hungarica, Vol. 10(3), pp. 51–64, 2013
  28. M. Anthony, J. Ratsaby. Maximal-margin case-based inference, Proc. of the 13th UK Workshop on Computational Intelligence (UKCI'13), pp. 112–119, Guildford, Surrey, U.K., Sept. 9–11, 2013
  29. U. Chester, J. Ratsaby. Machine learning image classification and clustering using a universal distance measure, in N. Brisaboa, O. Pedreira, P. Zezula (Eds.), Proc. of the 6th International Conference on Similarity Search and Applications (SISAP'13), Springer LNCS Vol. 8199, pp. 59–72, La Coruna, Spain, Oct. 2–4, 2013
  30. M. Anthony, J. Ratsaby. Quantifying accuracy of learning via sample width, Proc. of the IEEE Symposium on Foundations of Computational Intelligence (FOCI'13), pp. 84–90, Singapore, Apr. 16–19, 2013
  31. M. Anthony, J. Ratsaby. Analysis of a multi-category classifier, Discrete Applied Mathematics, Vol. 160(16), pp. 2329–2338, 2012
  32. M. Anthony, J. Ratsaby. Robust cutpoints in the logical analysis of numerical data, Discrete Applied Mathematics, Vol. 160(4), pp. 355–364, 2012
  33. J. Ratsaby. Combinatorial information distance, in C. Enachescu, F. Gheorghe Filip, B. Iantovics (Eds.), Advanced Computational Technologies, pp. 201–207, Editura Academiei Române (Romanian Academy Publishing House), Bucuresti, ISBN 978-973-27-2256-5, 2012
  34. 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
  35. 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), pp. 1–5, Eilat, Israel, Nov. 14–17, 2012
  36. 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
  37. J. Ratsaby. On the descriptional complexity of systems and their output response, Mathematics in Engineering, Science and Aerospace, Vol. 2(3), pp. 287–298, 2011
  38. J. Ratsaby. An empirical study of the complexity and randomness of prediction error sequences, Communications in Nonlinear Science and Numerical Simulation, Vol. 16, pp. 2832–2844, 2011
  39. J. Ratsaby. On the sysRatio and its critical point, Mathematical and Computer Modelling, Vol. 53, pp. 939–944, 2011
  40. J. Ratsaby. Information set-distance, Proc. of the 2010 Middle-European Conference on Applied Theoretical Computer Science (MATCOS'10), pp. 61–64, University of Primorska Press, Koper, Slovenia, Oct. 13–14, 2011
  41. J. Ratsaby. Prediction by compression, Proc. of the 8th IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPRA'11), pp. 282–288, Innsbruck, Austria, Feb. 16–18, 2011
  42. 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, Vol. 1(3), pp. 113–118, 2010
  43. M. Anthony, J. Ratsaby. Maximal width learning of binary functions, Theoretical Computer Science, Vol. 411, pp. 138–147, 2010
  44. J. Ratsaby, J. Chaskalovic. On the algorithmic complexity of static structures, Journal of Systems Science and Complexity, Vol. 23(6), pp. 1037–1053, 2010
  45. J. Chaskalovic, J. Ratsaby. Interaction of a self vibrating beam with chaotic external forces, Comptes Rendus Mécanique, Vol. 338(1), pp. 33–39, 2010
  46. 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'10), pp. 320–324, Eilat, Israel, Nov. 17–20, 2010
  47. J. Ratsaby. On the relation between a system's complexity and its interaction with random environments, Proc. of the International Symposium on Stochastic Models in Reliability Engineering, Life Sciences and Operations Management (SMRLO'10), pp. 893–901, Sami Shamoon College of Engineering, Be'er Sheva, Israel, Feb. 8–11, 2010
  48. J. Ratsaby. On the randomness in learning, Proc. of the 7th IEEE International Conference on Computational Cybernetics (ICCC'09), pp. 141–145, Palma de Mallorca, Spain, Nov. 26–29, 2009
  49. J. Ratsaby, J. Chaskalovic. Random patterns and complexity in static structures, in D. A. Karras et al. (Eds.), Proc. of the International Conference on Artificial Intelligence and Pattern Recognition (AIPR'09), pp. 255–261, Orlando, Florida, USA, July 13–16, 2009
  50. J. Ratsaby. Estimate of the number of restricted integer-partitions, Applicable Analysis and Discrete Mathematics, Vol. 2(2), pp. 222–233, 2008
  51. J. Ratsaby. On the complexity of binary samples, Annals of Mathematics and Artificial Intelligence, Vol. 52, pp. 55–65, 2008
  52. J. Ratsaby. An algorithmic complexity interpretation of Lin's third law of information theory, Entropy, Vol. 10(1), pp. 6–14, 2008
  53. B. Ycart, J. Ratsaby. VC dimensions of random function classes, Discrete Mathematics and Theoretical Computer Science, Vol. 10(1), pp. 113–128, 2008
  54. J. Ratsaby. Constrained versions of Sauer's lemma, Discrete Applied Mathematics, Vol. 156(14), pp. 2753–2767, 2008
  55. J. Ratsaby. On the complexity of constrained VC-classes, Discrete Applied Mathematics, Vol. 156(6), pp. 903–910, 2008
  56. J. Ratsaby. Density of smooth Boolean functions, Applicable Analysis and Discrete Mathematics, Vol. 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, Vol. 10(2), pp. 205–225, 2007
  58. B. Ycart, J. Ratsaby. The VC-dimension of k-uniform random hypergraphs, Random Structures and Algorithms, Vol. 30, pp. 564–572, 2007
  59. J. Ratsaby. Information efficiency, in J. van Leeuwen et al. (Eds.), Proc. of the 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, Vol. 363(1), pp. 2–10, 2006
  61. J. Ratsaby. On the combinatorial representation of information, in D. Z. Chen, D. T. Lee (Eds.), Proc. of the 12th International Computing and Combinatorics Conference (COCOON'06), Springer LNCS Vol. 4112, pp. 479–488, Taipei, Taiwan, Aug. 15–18, 2006
  62. J. Ratsaby. Complexity of VC-dimension classes of sequences with long repetitive runs, in J. F. Michon, P. Valarcher, J. B. Yunes (Eds.), Proc. of the 2nd International Workshop on Boolean Functions: Cryptography and Applications (BFCA'06), pp. 13–28, Presses Universitaires de Rouen et du Havre, Rouen, France, Mar. 13–15, 2006
  63. J. Ratsaby. On the complexity of samples for learning, in K. Y. Chwa, J. I. Munro (Eds.), Proc. of the 10th International Computing and Combinatorics Conference (COCOON'04), Springer LNCS Vol. 3106, pp. 198–209, Jeju Island, Korea, Aug. 17–20, 2004
  64. J. Ratsaby. A stochastic gradient descent algorithm for structural risk minimization, in K. P. Jantke, R. Gavaldà, E. Takimoto (Eds.), Proc. of the 14th International Conference on Algorithmic Learning Theory (ALT'03), Springer LNAI Vol. 2842, pp. 205–220, Sapporo, Japan, Oct. 17–19, 2003
  65. J. Ratsaby. On learning multicategory classification with sample queries, Information and Computation, Vol. 185(2), pp. 298–327, 2003
  66. J. Ratsaby, S. S. Venkatesh. On partially blind learning complexity, Proc. of the 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, Vol. 15(2), pp. 291–300, 1999
  68. J. Ratsaby, V. Maiorov. On the learnability of rich function classes, Journal of Computer and System Sciences, Vol. 58(1), pp. 183–192, 1999
  69. V. Maiorov, R. Meir, J. Ratsaby. On the approximation of functional classes equipped with a uniform measure using ridge functions, Journal of Approximation Theory, Vol. 99(1), pp. 95–111, 1999
  70. J. Ratsaby. Incremental learning with sample queries, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20(8), pp. 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, Vol. 86(1), pp. 81–93, 1998
  72. J. Ratsaby, V. Maiorov. On the value of partial information for learning by examples, Journal of Complexity, Vol. 13, pp. 509–544, 1998
  73. J. Ratsaby. An incremental nearest neighbor algorithm with queries, in M. I. Jordan, M. J. Kearns, 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 (Ed.), Proc. of the 3rd European Conference on Computational Learning Theory (ECOLT'97), Springer LNCS Vol. 1208, pp. 51–65, Jerusalem, Israel, Mar. 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 the 9th 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.), Proc. of the 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, Proc. of the 33rd Allerton Conference on Communication, Control, and Computing, pp. 1002–1009, Allerton, Illinois, USA, Oct. 4–6, 1995
Refereed papers presented at scientific conferences (no proceedings)
  1. J. Ratsaby. Information-theoretic characterizations of self-similar geometry, Midnight Sun Summit in Mathematics and Engineering, UiT, Narvik, Norway, 2026 (Slides)
  2. 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, Sept. 21–26, 2025 (Slides)
  3. J. Ratsaby. Parallel computations for learning from unstructured data processing, Symposium on Parallel Computing and Applications, Ariel University, Israel, June 15, 2023
  4. 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, Budapest, Hungary, June 27 – July 1, 2022 (Slides)
  5. A. Etinger, B. Kapilevich, B. Litvak, J. Ratsaby. Classification of mm-wave images obtained from noise-illuminated targets, International Congress of Imaging Science (ICIS'14), Tel Aviv, Israel, May 12–14, 2014
  6. M. Anthony, 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, Jan. 9–11, 2012
  7. J. Ratsaby. Randomness properties of statistical prediction, 55th Meeting of the Israel Physics Society (IPS'09), Bar Ilan University, Ramat-Gan, Israel, Dec. 13, 2009
  8. 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
  9. 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, Israel, Jan. 27 – Feb. 1, 2008
  10. J. Ratsaby. Density of smooth Boolean functions, International Mathematical Conference – Topics in Mathematical Analysis and Graph Theory (MAGT'06), University of Belgrade, Belgrade, Serbia, Sept. 1–4, 2006
  11. J. Ratsaby. Complexity of constrained VC-classes, International Scientific Annual Conference on Operations Research, Bremen, Germany, Sept. 7–9, 2005
  12. 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, U.K., July 19–21, 2004
  13. 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
  14. J. Ratsaby, G. Barnea. Automatic distributed intelligence: merging distributed computing with machine learning for internet-based intelligent applications, International Joint Conference on Artificial Intelligence (IJCAI'99), Workshop on Learning about Users, Stockholm, Sweden, July 31, 1999
  15. 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, May, 1994 (Abstract)