California Institute of Technology

P U B L I C A T I O N S

Selected Publications Over The Years:

Look for the paper whose title is one word. :-)

  • Automated Assessment of Right Atrial Pressure From Ultrasound Videos Using Machine Learning. D. Yurk et al., JACC: Advances, Vol. 3, No. 9, pages 1-10, September 2024. (link,pdf)
  • Resonance sonomanometry for noninvasive, continuous monitoring of blood pressure. R. Jimenez, et al., PNAS Nexus, vol 3, page 252, July 2024. (link,pdf)
  • The United States COVID-19 Forecast Hub dataset. E. Cramer, et al., Nature Scientific Data, 1;9(1):462, August 2022. (link,pdf)
  • Machine Learning for Recession Prediction and Dynamic Asset Allocation. A. James, Y. S. Abu-Mostafa and X. Qiao. The Journal of Financial Data Science, 2019, 1 (3) 41-56, Summer 2019. (abstract, paper)
  • Mismatched Training and Test Distributions Can Outperform Matched Ones. C. R. Gonzalez and Y. S. Abu-Mostafa. Neural Computation, MIT Press, 27:365–387, February 2015. (pdf)
  • Machines that think for themselves. Y. S. Abu-Mostafa. Scientific American, 289(7):78-81, July 2012. (pdf)
  • Learning From Data. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H-T. Lin, AMLbook.com, March 2012. (link)
  • The Bin Model. Y. S. Abu-Mostafa, X. Song, A. Nicholson, and M. Magdon-Ismail. Computer Science Technical Report Caltech CSTR:2004.002, California Institute of Technology, Jul. 2004. (abstract, pdf)
  • On the Maximum Drawdown of the Brownian Motion. M. Magdon-Ismail, A. F. Atiya, A. Pratap and Y. S. Abu-Mostafa. Journal of Applied Probability, Volume 41, Number 1, March, 2004. (pdf)
  • Financial Model Calibration Using Consistency Hints. Y. S. Abu-Mostafa. IEEE Trans. on Neural Networks, 12(4):791–808, July 2001. (pdf)
  • Maximal Codeword Lengths in Huffman Codes . Y. S. Abu-Mostafa and R. J. McEliece. Computers and Mathematics with Applications, Elsevier, 39(11):129-134, 2000. (pdf)
  • Computational Finance. Y. S. Abu-Mostafa, B. LeBaron, A. Lo, and A. Weigend (eds.), MIT Press, 2000. (link)
  • Financial Markets: Very Noisy Information Processing. M. Magdon-Ismail, A. Nicholson and Y. S. Abu-Mostafa. Proceedings of the IEEE, 86(11): 2184–2195, Nov. 1998. (pdf,link)
  • Validation of Volatility Models. M. Magdon-Ismail and Y. S. Abu-Mostafa. Journal of Forecasting, 17:349–368, Sep.-Nov. 1998. (pdf)
  • Decision Technologies for Financial Engineering. A. Weigend, Y. S. Abu-Mostafa, and A. Refenes (eds.), World Scientific, 1997. (link)
  • Incorporating Contextual Information into White Blood Cell Recognition. X. Song, Y. S. Abu-Mostafa, J. Sill, and H. Kasdan. NIPS: Advances in Neural Information Processing Systems 9, pp. 950-956, MIT Press, 1997. (pdf)
  • Monotonicity Hints. J. Sill and Y. S. Abu-Mostafa. NIPS: Advances in Neural Information Processing Systems 9, 643-640, MIT Press, 1996. (pdf)
  • Introduction to Financial Forecasting. Y. S. Abu-Mostafa and A. Atiya. Applied Intelligence, 6(3):205–213, Jul. 1996. (pdf)
  • Neural Networks in Financial Engineering. A. Refenes, Y. S. Abu-Mostafa, J. Moody, and A. Weigend (eds.), World Scientific, 1996. (link)
  • Hints. Y. S. Abu-Mostafa. Neural Computation, 7:639–671, July 1995. (pdf,link)
  • Financial Applications of Learning from Hints. Y. S. Abu-Mostafa, NIPS: Advances in Neural Information Processing Systems 7, MIT Press, pp. 411-418, 1995. (pdf)
  • Machines That Learn from Hints. Y. S. Abu-Mostafa. Scientific American, 272(4):64–69, Apr. 1995. (pdf)
  • A method for learning from hints. Y. S. Abu-Mostafa. NIPS: Advances in Neural Information Processing Systems 5, S. Hanson et al (eds.), pp. 73-80, Morgan Kaufmann, 1993. (pdf)
  • Hints and the VC Dimension. Y. S. Abu-Mostafa. Neural Computation, 5:278–288, MIT Press, March 1993. (pdf)
  • Analog Neural Networks as Decoders. R. Erlanson and Y. S. Abu-Mostafa. NIPS: Advances in Neural Information Processing Systems 3, R.P. Lippmann et al. (ed.), pp. 585-588, Morgan Kaufmann, 1990. (pdf)
  • Learning from Hints in Neural Networks. Y. S. Abu-Mostafa. Journal of complexity, 6:192–198, Jun. 1990. (pdf)
  • A method for the associative storage of analog vectors. A. Atiya and Y. S. Abu-Mostafa. NIPS: Advances in Neural Information Processing Systems 2, D. Touretzky (ed.), pp. 590-595, Morgan Kaufmann, 1989. (pdf)
  • The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning. Y. S. Abu-Mostafa. Neural Computation, 1:312–317, MIT Press, 1989. (pdf)
  • Information Theory, Complexity and Neural Networks. Y. S. Abu-Mostafa. IEEE Communications Magazine, 27 (11). 25-28, 82, 1989. (pdf)
  • On the k-winners-take-all network. E. Majani, R. Erlanson and Y. S. Abu-Mostafa. NIPS: Advances in Neural Information Processing Systems 1, D. Touretzky (ed.), pp. 634-642, Morgan Kaufmann, 1988. (pdf)
  • Connectivity versus Entropy. Y. S. Abu-Mostafa. NIPS: Neural Information Processing Systems, D. Anderson (ed.), pp. 1-8, American Institute of Physics, 1988. (pdf)
  • Complexity in Information Theory. Y. S. Abu-Mostafa (ed.), Springer-Verlag, 1988. (link)
  • Optical neural computers. Y. S. Abu-Mostafa and D. Psaltis. Scientific American, vol. 256, number 3, pp. 88-95, March 1987. (pdf)
  • The complexity of information extraction. Y. S. Abu-Mostafa. IEEE Trans. on Information Theory, vol. IT-32, pp. 513-525, July 1986. (pdf)
  • Information Capacity of the Hopfield Model. Y. S. Abu-Mostafa and J. St. Jaques. IEEE Trans. on Information Theory, IT-32:513–525, Jul. 1986. (pdf)
  • Recognitive Aspects of Moment Invariants. Y. S. Abu-Mostafa and D. Psaltis. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-6:698–706, Nov. 1984. ((pdf)
  • A Differentiation Test for Absolute Convergence. Y. S. Abu-Mostafa. Mathematics Magazine, vol. 57, pp. 228-231, September 1984. (pdf)