List of Publications
Refereed journal papers^{‡}
 J. Ratsaby. Fractal oracle numbers, Fractals—Complex Geometry, Patterns, and Scaling in Nature and Society, vol. 32(1), Article No. 2450029, 2024.
 J. Ratsaby. Learning halfspaces on general infinite spaces equipped with a distance function, Information and Computation, Vol. 291, Article No. 105008,2023.
 M. Anthony, J. Ratsaby. Largewidth (LW) machine learning algorithm, Progress in Artificial Intelligence , Vol. 9, pp.275285, 2020.
 A. Belousov, J. Ratsaby. A Parallel Computation Algorithm for Image Feature Extraction,
Journal of Advances in Applied and Computational Mathematics, Vol. 6, pp. 118, 2019.
 J. Ratsaby. On deterministic finite state machines in random environments, Probability in the Engineering and Informational Sciences, Vol. 33(4), pp. 528563, 2019.
 M. Anthony, J. Ratsaby. Large width nearest prototype classification on general distance spaces, Theoretical Computer Science, Vol. 738, pp. 6579, 2018.
 M. Anthony, J. Ratsaby. Largewidth bounds for learning halfspaces on distance spaces, Discrete Applied Mathematics, vol. 243, pp. 7389, 2018.
 M. Anthony, J. Ratsaby. Classification based on prototypes with spheres of influence, Information and Computation, vol. 256, pp. 372380, 2017.
 M. Anthony, J. Ratsaby. Multicategory classifiers and sample width, Journal of Computer and System Sciences, vol. 82(8), pp. 12231231, 2016.
 M. Anthony, J. Ratsaby. A probabilistic approach to casebased inference, Theoretical Computer Science, vol. 589, pp. 6175, 2015.
 M. Anthony, J. Ratsaby. Learning bounds via sample width for classifiers on finite metric spaces, Theoretical Computer Science, Vol. 529, Pages 210, 2014.
 M. Anthony and J. Ratsaby. A hybrid classifier based on boxes and nearest neighbors, Discrete Applied Mathematics, Vol. 529, Pages 210, 2014.
 L. Kovacs, J. Ratsaby. Analysis of linear interpolation of fuzzy sets with entropybased distances, Acta Polytechnica Hungarica, 10(3), pp. 5164, 2013.
 M. Anthony, J. Ratsaby. Analysis of a multicategory classifier,
Discrete Applied Mathematics, 160(16), pp. 23292338, 2012.
 M. Anthony, J. Ratsaby. Robust cutpoints in the logical analysis of numerical data, Discrete Applied Mathematics, 160(4), pp. 355364, 2012.
 J. Ratsaby. On the descriptional complexity of systems and their output response, Mathematics in Engineering, Science and Aerospace, 2(3), pp. 287298, 2011.
 J. Ratsaby. An empirical study of the complexity and randomness of prediction error sequences, Communications in Nonlinear Science and Numerical Simulations, 16, pp. 28322844, 2011.
 J. Ratsaby. On the sysRatio and its critical point, Mathematical and Computer Modeling, 53, pp. 939944, 2011.
 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. 113118, 2010.

M. Anthony, J. Ratsaby. Maximal width learning of binary functions, Theoretical Computer Science, 411, pp. 138147, 2010.
 J. Ratsaby, J. Chaskalovic. On the algorithmic complexity of static structures, Journal of Systems Science and Complexity, 23(6), pp. 10371053, 2010.
 J. Chaskalovic, J. Ratsaby. Interaction of a self vibrating beam with chaotic external forces, Comptes Rendus Mecanique, 338(1), pp. 3339, 2010.
 J. Ratsaby. Estimate of the number of restricted integerpartitions,
Applicable Analysis and Discrete Mathematics, 2(2): 222233, 2008
 J. Ratsaby. On the complexity of binary samples, Annals of Mathematics and Artificial Intelligence, 52, pp. 5565, 2008
 J. Ratsaby. An Algorithmic Complexity Interpretation of Lin's Third Law of Information Theory, Entropy, 10(1), pp. 614, 2008
 B. Ycart, J. Ratsaby. VC and related dimensions of random function classes, Discrete Mathematics and Theoretical Computer Science, 10(1),pp. 113128, 2008
 J. Ratsaby. Constrained versions of Sauer's lemma, Discrete Applied Mathematics, 156(14), pp. 27532767, 2008
 J. Ratsaby. On the complexity of constrained VCclasses, Discrete Applied Mathematics, 156(6), pp. 903910, ,2008
 J. Ratsaby. Density of smooth Boolean functions, Applicable Analysis and Discrete Mathematics, 1(1), pp. 184198, 2007
 J. Ratsaby. On the VCdimension and Boolean functions with long runs. Journal of Discrete Mathematical Sciences and Cryptography, 10(2), pp. 205225, 2007
 B. Ycart, J. Ratsaby. The VCdimension of kuniform random hypergraphs, Random Structures and Algorithms, 30, pp. 564572, 2007
 J. Ratsaby. Complexity of hyperconcepts. Theoretical Computer Science, 363(1), pp. 210, 2006
 J. Ratsaby. On learning multicategory classification with sample queries. Information and Computation, 185(2), pp. 298327, 2003
 V. Maiorov, J. Ratsaby. On the degree of approximation by manifolds of finite pseudodimension. Constructive Approximation, 15(2), pp. 291300, 1999
 J. Ratsaby, V. Maiorov. On the learnability of rich function classes. Journal of Computer and System Sciences, 58(1), pp. 183192, 1999
 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. 95111, 1999
 J. Ratsaby. Incremental learning with sample queries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 883888, 1998
 V. Maiorov, J. Ratsaby, The degree of approximation of sets in Euclidean space using sets with bounded VapnikChervonenkis dimension. Discrete Applied Mathematics, 86(1), pp. 8193, 1998
 J. Ratsaby, V. Maiorov. On the value of partial information for learning by examples. Journal of Complexity, 13, pp. 509544, 1998
Chapters in books
 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 9789732722565, 2012
 J. Ratsaby. Valiant's PAC Model of learning, In MingYang Kao (Ed.), Encyclopedia of Algorithms, pp..622624, ISBN 9780387307701, Springer, (invited chapter), 2008
Refereed papers in conference proceedings
 J. Ratsaby, A. Timashkov, MultiGPU 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
 J. Ratsaby, A. Timashkov, Accelerating the LZcomplexity algorithm, Proc. of 29th International Conference on Parallel and Distributed Systems (ICPADS’23), Ocean Flower Island, Hainan, China, December 1721, 2023
 J. Ratsaby, A, Sabaty, Parallelizing the LargeWidth learning algorithm, Proc. of International Conference on the Science of Electrical Engineering (ICSEE'2018),Eilat, ISRAEL, Dec. 1214, 2018
 J. Ratsaby, On how complexity affects the stability of a predictor, in A. Storkey, F. PerezCruz (Eds.), Proc. of Machine Learning Research, Vol. 84, pp. 161167, Artificial Intelligence and Statistics (AISTATS2018), Playa Blanca, Lanzarote, Canary Islands, April 911, 2018
 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), ETHZurich, pp. 162166, Feb. 2123, 2018
 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, SaintEtienne, France, Oct. 2224, 2015
 J. Ratsaby, On complexity and randomness of Markovchain prediction, Proc. of IEEE Information Theory Workshop (ITW '15), Jerusalem, Israel, April 26  May 1, 2015
 A. Belousov, J. Ratsaby. Massively Parallel Computations of the LZcomplexity of Strings, Proc. of the 28th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'14), pp. 15, Eilat, Israel, Dec. 35, 2014
 L. Kovacs, J. Ratsaby, A New PseudoMetric 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. 205216, Zakopane, Poland, June 15, 2014
 M. Anthony, J. Ratsaby. Maximalmargin casebased inference, Proc. of 13th UK Workshop on Computational Intelligence, (UKCI2013), Sept. 911, 2013
 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. 5972, La Coruna, Spain, Oct. 24, 2013
 M. Anthony, J. Ratsaby. Quantifying accuracy of learning via sample width, Proc. of the IEEE Symposium on Foundations of Computational Intelligence (FOCI'13), pp. 17, Singapore, April 1619, 2013
 J. Ratsaby, V. Sirota. FPGAbased data compressor based on Prediction by Partial Matching, Proc. of the 27th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'12), pp. 15, Eilat, Israel, Nov. 1417, 2012
 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. 15, Eilat, Israel, Nov. 1417, 2012
 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. 14, Eilat, Israel, Nov. 1417, 2012, 2012
 J. Ratsaby. Information SetDistance,
Proc. of the 2010 MiniConference on Applied Theoretical Computer Science (MATCOS2010), pp. 6164, University of Primorska Press, Koper, Slovenia, Oct. 1314, 2011
 J. Ratsaby. Prediction by Compression, Proc. of Eighth IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPRA 2011), pp. 282288, Insbruck, Austria, Feb. 1618, 2011
 J. Ratsaby, D. Zavielov. An FPGAbased pattern classifier using data compression, Proc. of the 26th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI 2010), pp. 320324, Eilat, Israel, Nov. 1720, 2010
 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.893901, Sami Shamoon College of Engineering, Be'er Sheva, Israel, February 811, 2010
 J. Ratsaby. On the randomness in learning. Proceedings of the 7th IEEE International Conference on Computational Cybernetics (ICCC'09), pp. 141145, Palma de Mallorca, Spain, Nov. 26  29, 2009
 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. 255261, Orlando, Florida, USA, July 1316, 2009
 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. 475487, Harrachov, Czech Republic, Jan. 20  26, 2007
 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), SpringerVerlag LNCS 4112, pp. 479488, Taipei, Taiwan,, August 1518, 2006
 J. Ratsaby. Complexity of VCdimension 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. 1328, Presses Universitaires de Rouen et du Havre, Rouen, France, March 1315, 2006
 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. 198209, Jeju Island, Korea, August 1720, 2004
 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. 205220, Sapporo, Japan, October 1719, 2003
 J. Ratsaby, S.S. Venkatesh. On partially blind learning complexity. In Proc. of IEEE International Symposium on Circuits and Systems (ISCAS'08), pp. II765768, Geneva, Switzerland, May 2831, 2000
 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
 J. Ratsaby, V. Maiorov. Generalization of the PACmodel for incomplete side information. In S. BenDavid, editor, Proceedings 3rd European Conference on Computational Learning Theory (ECOLT'97), Springer LNCS 1208, pp. 5165, Jerusalem, Israel, March 1719, 1997
 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
 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 58, 1995
 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. 10021009, Allerton, Illinois, USA, October 46, 1995
Refereed papers presented at scientific conferences (no proceedings)

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
 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 911, 2012
 J. Ratsaby. Randomness properties of statistical prediction. The 55th meeting of the Israel physics society (IPS'09), Bar Ilan University, RamatGan, Israel, Dec. 13, 2009
 J. Ratsaby. Some consequences of the complexity of intelligent prediction, International symposium on understanding intelligent and complex systems (UICS'09), Petru Maior University of TarguMures, Romania, Oct. 2223, 2009
 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
 J. Ratsaby. Density of Smooth Boolean Functions, International Mathematical Conference  Topics in Mathematical Analysis and Graph Theory (MAGT'06), University of Belgrade, Belgrade, Sept. 14, 2006
 J. Ratsaby. Complexity of constrained VCClasses. International Scientific Annual Conference on Operations Research, Bremen, Germany, September 79, 2005
 J. Ratsaby. A Sharp Threshold Result for VC Classes of LargeMargin Functions, EU PASCAL Workshop on Learning Theoretic and Bayesian Inductive Principles, Gatsby Computational Neuroscience Unit, University College London, London, July 1921, 2004
 J. Ratsaby. Meeting the challenges of ebusiness by distributed artificial intelligence, BarIlan International Symposium on the Foundations of Artificial Intelligence, Honoring: Yaacov Choueka, RamatGan, Israel, June 2527, 2001
 J. Ratsaby, G. Barnea. Automatic distributed intelligence: Merging distributed computing with machine learning for internetbased intelligent applications, International Joint Conference of Artificial Intelligence (IJCAI'99), Workshop on Learning about Users, Stockholm, Sweden, July 31, 1999
 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
 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.}