ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph's algebraic spectru...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
We consider a supervised machine learning scenario where labels are provided by a heterogeneous set of teachers, some of which are mediocre, incompetent, or perhaps even malicious...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
An anytime algorithm is capable of returning a response to the given task at essentially any time; typically the quality of the response improves as the time increases. Here, we c...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
We propose abc-boost (adaptive base class boost) for multi-class classification and present abc-mart, an implementation of abcboost, based on the multinomial logit model. The key ...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledg...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text c...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logica...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
We propose an importance weighting framework for actively labeling samples. This technique yields practical yet sound active learning algorithms for general loss functions. Experi...