2 research outputs found
Learning about individuals from group statistics
We propose a new problem formulation which is similar to, but more
informative than, the binary multiple-instance learning problem. In this
setting, we are given groups of instances (described by feature vectors) along
with estimates of the fraction of positively-labeled instances per group. The
task is to learn an instance level classifier from this information. That is,
we are trying to estimate the unknown binary labels of individuals from
knowledge of group statistics. We propose a principled probabilistic model to
solve this problem that accounts for uncertainty in the parameters and in the
unknown individual labels. This model is trained with an efficient MCMC
algorithm. Its performance is demonstrated on both synthetic and real-world
data arising in general object recognition.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Self-Avoiding Random Dynamics on Integer Complex Systems
This paper introduces a new specialized algorithm for equilibrium Monte Carlo
sampling of binary-valued systems, which allows for large moves in the state
space. This is achieved by constructing self-avoiding walks (SAWs) in the state
space. As a consequence, many bits are flipped in a single MCMC step. We name
the algorithm SARDONICS, an acronym for Self-Avoiding Random Dynamics on
Integer Complex Systems. The algorithm has several free parameters, but we show
that Bayesian optimization can be used to automatically tune them. SARDONICS
performs remarkably well in a broad number of sampling tasks: toroidal
ferromagnetic and frustrated Ising models, 3D Ising models, restricted
Boltzmann machines and chimera graphs arising in the design of quantum
computers.Comment: 22 pages. 9 figure