no code implementations • NeurIPS 2016 • Nguyen Viet Cuong, Huan Xu
We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning.
no code implementations • 30 Mar 2016 • Nguyen Viet Cuong, Nan Ye, Wee Sun Lee
This suggests we should use a Lipschitz utility for AL if robustness is required.
no code implementations • 12 Aug 2014 • Vu Dinh, Lam Si Tung Ho, Nguyen Viet Cuong, Duy Nguyen, Binh T. Nguyen
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution.
no code implementations • 12 Jun 2014 • Nguyen Viet Cuong, Lam Si Tung Ho, Vu Dinh
For the generalization of the algorithm, we prove a PAC-style bound on the training sample size for the expected $L_1$-loss to converge to the optimal loss when training data are V-geometrically ergodic Markov chains.
no code implementations • NeurIPS 2013 • Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu
We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses.