We revisit recently proposed algorithms for probabilistic clustering with pair-wise constraints between data points. We evaluate and compare existing techniques in terms of robust...
The four-level pachinko allocation model (PAM) (Li & McCallum, 2006) represents correlations among topics using a DAG structure. It does not, however, represent a nested hiera...
A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic princip...
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
This paper investigates compression of 3D objects in computer graphics using manifold learning. Spectral compression uses the eigenvectors of the graph Laplacian of an object'...
Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant ...
Relational data appear frequently in many machine learning applications. Relational data consist of the pairwise relations (similarities or dissimilarities) between each pair of i...
Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip...
Distance metric learning and nonlinear dimensionality reduction are two interesting and active topics in recent years. However, the connection between them is not thoroughly studi...
High dimensionality of POMDP's belief state space is one major cause that makes the underlying optimal policy computation intractable. Belief compression refers to the method...
Xin Li, William Kwok-Wai Cheung, Jiming Liu, Zhili...
Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum,...