Machine Learning Papers

 

  • (2012)  Eric Mjolsness, Compositional stochastic modeling and probabilistic programming. Workshop on Probabilistic Programming, Neural Information Processing Systems Conference Workshops, extended abstract, December 2012. [Mjolsness_1212.0582] Also available as [arXiv:1212.0582]
  • (2010)  Wang, Y., Christley, S., Mjolsness, E., and Xie, X. Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent , BMC Systems Biology 4:99 . [ Published PDF ]
  • (2003)  Clustering analysis of microarray gene expression data by splitting algorithm . Ruye Wang, Lucas Scharenbroich, Christopher Hart, Barbara Wold, and Eric Mjolsness. Journal of Parallel and Distributed Computing, Volume 63, Numbers 7-8, pp. 692-706, July-August 2003. [ Preprint ]
  • (2001) Machine learning for science: State of the art and future prospects. Eric Mjolsness and Dennis DeCoste, Science 293, 2051-2055, September 14, 2001. [ Paper ]
  • (1999)  From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data. E. Mjolsness, T. Mann, R. Castaño, and B. Wold. Advances in Neural Information Processing Systems 1999. [Paper]
  • (1996)  Learning with preknowledge: Clustering with point and graph matching distance measures. Steven Gold, Anand Rangarajan, and Eric Mjolsness, Neural Computation , vol 8 no 4, May 15 1996. Reprinted in Unsupervised Learning: Foundations of Neural Computation , eds. G. Hinton and T. J. Sejnowski, MIT Press 1999. [Journal paper | PDF Preprint | Postscript Preprint ]
  • (1994)  Clustering with a Domain-Specific Distance Measure. Steven Gold, Anand Rangarajan, and Eric Mjolsness, Advances in Neural Information Processing Systems 6 , editors Cowan, Tesauro, Alspector, Morgan-Kaufmann 1994. [ Preprint ]
  • (1989)  Scaling, machine learning, and genetic neural nets, Eric Mjolsness, David H. Sharp, and Bradley K. Alpert. Advances in Applied Mathematics, June 1989. [ Paper ]