Now showing items 1-8 of 8

    • DNA Binding and Games 

      Perez-Breva, Luis; Ortiz, Luis E.; Yeang, Chen-Hsiang, 1969-; Jaakkola, Tommi (2006-03-06)
      We propose a game-theoretic approach tolearn and predict coordinate binding of multiple DNA bindingregulators. The framework implements resource constrainedallocation of proteins to local neighborhoods as well as to ...
    • Generalized Low-Rank Approximations 

      Srebro, Nathan; Jaakkola, Tommi (2003-01-15)
      We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving {\\em weighted} low rank approximation problems, which, unlike simple ...
    • Managing the 802.11 Energy/Performance Tradeoff with Machine Learning 

      Monteleoni, Claire; Balakrishnan, Hari; Feamster, Nick; Jaakkola, Tommi (2004-10-27)
      This paper addresses the problem of managing the tradeoff betweenenergy consumption and performance in wireless devices implementingthe IEEE 802.11 standard. To save energy, the 802.11 specificationproposes a power-saving ...
    • Maximum Entropy Discrimination 

      Jaakkola, Tommi; Meila, Marina; Jebara, Tony (1999-12-01)
      We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific ...
    • Mean Field Theory for Sigmoid Belief Networks 

      Saul, Lawrence K.; Jaakkola, Tommi; Jordan, Michael I. (1996-08-01)
      We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it ...
    • On the Convergence of Stochastic Iterative Dynamic Programming Algorithms 

      Jaakkola, Tommi; Jordan, Michael I.; Singh, Satinder P. (1993-08-01)
      Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton ...
    • Online Learning of Non-stationary Sequences 

      Monteleoni, Claire; Jaakkola, Tommi (2005-11-17)
      We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. We derive upper and lower relative loss bounds for a class of universal learning algorithms involving ...
    • Stable Mixing of Complete and Incomplete Information 

      Corduneanu, Adrian; Jaakkola, Tommi (2001-11-08)
      An increasing number of parameter estimation tasks involve the use of at least two information sources, one complete but limited, the other abundant but incomplete. Standard algorithms such as EM (or em) used in this context ...