Woosang Lim

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For robust spoken dialog management, various dialog state tracking methods have been proposed. Although discriminative models are gaining popularity due to their superior performance, generative models based on the Partially Observable Markov Decision Process model still remain attractive since they provide an integrated framework for dialog state tracking(More)
How can we scale-up logistic regression, or L1 regularized loss minimization in general, for Terabyte-scale data which do not fit in the memory? How to design the distributed algorithm efficiently? Although there exist two major algorithms for logistic regression, namely Stochastic Gradient Descent (SGD) and Stochastic Coordinate Descent (SCD), they face(More)
The Nyström method has been one of the most effective techniques for kernel-based approach that scales well to large data sets. Since its introduction , there has been a large body of work that improves the approximation accuracy while maintaining computational efficiency. In this paper , we present a novel Nyström method that improves both accuracy and(More)
Bayesian reinforcement learning (BRL) provides a formal framework for optimal exploration-exploitation tradeoff in reinforcement learning. Unfortunately, it is generally intractable to find the Bayes-optimal behavior except for restricted cases. As a consequence, many BRL algorithms, model-based approaches in particular, rely on approximated models or(More)
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