20,264 research outputs found
Kernel discriminant analysis and clustering with parsimonious Gaussian process models
This work presents a family of parsimonious Gaussian process models which
allow to build, from a finite sample, a model-based classifier in an infinite
dimensional space. The proposed parsimonious models are obtained by
constraining the eigen-decomposition of the Gaussian processes modeling each
class. This allows in particular to use non-linear mapping functions which
project the observations into infinite dimensional spaces. It is also
demonstrated that the building of the classifier can be directly done from the
observation space through a kernel function. The proposed classification method
is thus able to classify data of various types such as categorical data,
functional data or networks. Furthermore, it is possible to classify mixed data
by combining different kernels. The methodology is as well extended to the
unsupervised classification case. Experimental results on various data sets
demonstrate the effectiveness of the proposed method
Synthetic learner: model-free inference on treatments over time
Understanding of the effect of a particular treatment or a policy pertains to
many areas of interest -- ranging from political economics, marketing to
health-care and personalized treatment studies. In this paper, we develop a
non-parametric, model-free test for detecting the effects of treatment over
time that extends widely used Synthetic Control tests. The test is built on
counterfactual predictions arising from many learning algorithms. In the
Neyman-Rubin potential outcome framework with possible carry-over effects, we
show that the proposed test is asymptotically consistent for stationary, beta
mixing processes. We do not assume that class of learners captures the correct
model necessarily. We also discuss estimates of the average treatment effect,
and we provide regret bounds on the predictive performance. To the best of our
knowledge, this is the first set of results that allow for example any Random
Forest to be useful for provably valid statistical inference in the Synthetic
Control setting. In experiments, we show that our Synthetic Learner is
substantially more powerful than classical methods based on Synthetic Control
or Difference-in-Differences, especially in the presence of non-linear outcome
models
- …