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Wee Lee

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5 papers

1 author row

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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NeurIPS Conference 2009 Conference Paper

Conditional Random Fields with High-Order Features for Sequence Labeling

Nan Ye
Wee Lee
Hai Chieu
Dan Wu

Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems. However, only dependencies between adjacent labels are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer distance dependencies are taken into account. In this paper, we show that it is possible to design efficient inference algorithms for a conditional random field using features that depend on long consecutive label sequences (high-order features), as long as the number of distinct label sequences in the features used is small. This leads to efficient learning algorithms for these conditional random fields. We show experimentally that exploiting dependencies using high-order features can lead to substantial performance improvements for some problems and discuss conditions under which high-order features can be effective.

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NeurIPS Conference 2007 Conference Paper

Cooled and Relaxed Survey Propagation for MRFs

Hai Chieu
Wee Lee
Yee Teh

We describe a new algorithm, Relaxed Survey Propagation (RSP), for ﬁnding MAP conﬁgurations in Markov random ﬁelds. We compare its performance with state-of-the-art algorithms including the max-product belief propagation, its se- quential tree-reweighted variant, residual (sum-product) belief propagation, and tree-structured expectation propagation. We show that it outperforms all ap- proaches for Ising models with mixed couplings, as well as on a web person disambiguation task formulated as a supervised clustering problem.

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NeurIPS Conference 2007 Conference Paper

What makes some POMDP problems easy to approximate?

Wee Lee
Nan Rong
David Hsu

Point-based algorithms have been surprisingly successful in computing approx- imately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimensional belief spaces. In this work, we seek to understand the belief-space properties that allow some POMDP problems to be approximated efﬁciently and thus help to explain the point-based algorithms’ success often ob- served in the experiments. We show that an approximately optimal POMDP so- lution can be computed in time polynomial in the covering number of a reachable belief space, which is the subset of the belief space reachable from a given belief point. We also show that under the weaker condition of having a small covering number for an optimal reachable space, which is the subset of the belief space reachable under an optimal policy, computing an approximately optimal solution is NP-hard. However, given a suitable set of points that “cover” an optimal reach- able space well, an approximate solution can be computed in polynomial time. The covering number highlights several interesting properties that reduce the com- plexity of POMDP planning in practice, e. g. , fully observed state variables, beliefs with sparse support, smooth beliefs, and circulant state-transition matrices.

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NeurIPS Conference 2006 Conference Paper

Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

Xinhua Zhang
Wee Lee

Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graphs. We propose a graph learning method for the harmonic energy minimization method; this is done by minimizing the leave-one-out prediction error on labeled data points. We use a gradient based method and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation. Experimental results show that the graph learning method is effective in improving the performance of the classification algorithm.

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