Unifying Divergence Minimization and Statistical Inference Via Convex Duality
Measuring Statistical Dependence with Hilbert-Schmidt Norms
Feature hashing for large scale multitask learning
Improving maximum margin matrix factorization
A scalable modular convex solver for regularized risk minimization
Algorithmic Learning Theory
Lecture Notes in Computer Science
Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09
Machine Learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’07
Olivier Bousquet
Yasemin Altun
Arthur Gretton
Alexandros Karatzoglou
Josh Attenberg
Markus Weimer
Matrix Sketching Over Sliding Windows
A kernel-based causal learning algorithm
Toward a Unified Theory of Sparse Dimensionality Reduction in Euclidean Space
StatSnowball
Classifying matrices with a spectral regularization
An accelerated gradient method for trace norm minimization