The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
We formulate and study a new variant of the k-armed bandit problem, motivated by e-commerce applications. In our model, arms have (stochastic) lifetime after which they expire. In...
Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski,...
Practitioners and researchers often refer to error rates or accuracy percentages of databases. The former is the number of cells in error divided by the total number of cells; the...
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by...
We present a new family of linear time algorithms based on sufficient statistics for string comparison with mismatches under the string kernels framework. Our algorithms improve t...
: Poor Information Quality (IQ) has a significant impact on the general performance of an organization. Investments in IT have had a great impact on the banking industry in recent ...
The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (i.e., generate nice-looking samples of) several very hi...
Ilya Sutskever, Geoffrey E. Hinton, Graham W. Tayl...
We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. Change-point analysis of an unlabelled sample of observations consists in,...