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Nguyen Cuong

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2 papers

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NeurIPS Conference 2016 Conference Paper

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint

Nguyen 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. We investigate the near-optimality of greedy algorithms for this problem with both modular and non-modular cost functions. In both cases, we prove that two simple greedy algorithms are not near-optimal but the best between them is near-optimal if the utility function satisfies pointwise submodularity and pointwise cost-sensitive submodularity respectively. This implies a combined algorithm that is near-optimal with respect to the optimal algorithm that uses half of the budget. We discuss applications of our theoretical results and also report experiments comparing the greedy algorithms on the active learning problem.

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AAAI Conference 2016 Conference Paper

Robustness of Bayesian Pool-Based Active Learning Against Prior Misspecification

Nguyen Cuong
Nan Ye
Wee Lee

We study the robustness of active learning (AL) algorithms against prior misspeciﬁcation: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are robust (i. e. , near α-approximate) if the utility is Lipschitz continuous in the prior. We further show that robustness may not be achieved if the utility is non-Lipschitz. This suggests we should use a Lipschitz utility for AL if robustness is required. For the minimum cost setting, we can also obtain a robustness result for approximate AL algorithms. Our results imply that many commonly used AL algorithms are robust against perturbed priors. We then propose the use of a mixture prior to alleviate the problem of prior misspeciﬁcation. We analyze the robustness of the uniform mixture prior and show experimentally that it performs reasonably well in practice.

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