We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
The Dempster-Shafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) whe...
In this paper we propose a novel analog design optimization methodology to address two key aspects of top-down system-level design: (1) how to optimally compare and select analog ...
Xin Li, Jian Wang, Lawrence T. Pileggi, Tun-Shih C...
Discovery of graphical models is NP-hard in general, which justifies using heuristics. We consider four commonly used heuristics. We summarize the underlying assumptions and anal...
We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functi...