Add to ORKGThe versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions when the optimal parameter vector is sparse. This work characterizes a certain strong convexity property of general exponential families, which allows their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L1 regularization
There has been an explosion of interest in statistical models for analyzing network data, and consid...
Exponential families of distributions are parametric dominated families in which the logarithm of pr...
This paper studies a class of exponential family models whose canonical parameters are specified as ...
The versatility of exponential families, along with their attendant convexity properties, make them ...
The versatility of exponential families, along with their attendant convexity properties, make them ...
This is not a copy of the original, which is in the University of Washington library because the or...
In this paper, we study the large-sample properties of the posterior-based inference in the curved e...
Summary This work studies the large sample properties of the posterior-based inference in the curved...
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization t...
We propose a class of closed-form estimators for sparsity-structured graphical models, expressed as ...
Exponential varieties arise from exponential families in statistics. These real algebraic varieties ...
Previous work has examined structure learning in log-linear models with `1-regularization, largely f...
This thesis considers estimation and statistical inference for high dimensional model with constrain...
One dimensional exponential families on finite sample spaces are studied using the geometry of the s...
We present a new approach to learning the structure and parameters of a Bayesian network based on re...
There has been an explosion of interest in statistical models for analyzing network data, and consid...
Exponential families of distributions are parametric dominated families in which the logarithm of pr...
This paper studies a class of exponential family models whose canonical parameters are specified as ...
The versatility of exponential families, along with their attendant convexity properties, make them ...
The versatility of exponential families, along with their attendant convexity properties, make them ...
This is not a copy of the original, which is in the University of Washington library because the or...
In this paper, we study the large-sample properties of the posterior-based inference in the curved e...
Summary This work studies the large sample properties of the posterior-based inference in the curved...
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization t...
We propose a class of closed-form estimators for sparsity-structured graphical models, expressed as ...
Exponential varieties arise from exponential families in statistics. These real algebraic varieties ...
Previous work has examined structure learning in log-linear models with `1-regularization, largely f...
This thesis considers estimation and statistical inference for high dimensional model with constrain...
One dimensional exponential families on finite sample spaces are studied using the geometry of the s...
We present a new approach to learning the structure and parameters of a Bayesian network based on re...
There has been an explosion of interest in statistical models for analyzing network data, and consid...
Exponential families of distributions are parametric dominated families in which the logarithm of pr...
This paper studies a class of exponential family models whose canonical parameters are specified as ...