Sue Ann Hong

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We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city” and “university.” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of(More)
Text learning algorithms are reasonably successful when provided with enough labeled or annotated training examples. For instance, text classifiers [13, 10, 21, 4, 18] reach high accuracy from large sets of class-labeled documents; information extraction algorithms [3, 15, 19, 8] perform well when given many tagged documents or large sets of rules as input.(More)
Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice due to the need for exploring a large feature space. In this paper, we propose a coarse-to-fine hierarchical approach for encoding prior knowledge that drastically reduces the amount of exploration(More)
Market-based algorithms have become popular in collaborative multi-agent planning due to their simplicity, distributedness, low communication requirements, and proven success in domains such as task allocation and robotic exploration. Most existing marketbased algorithms, however, suffer from two main drawbacks: resource prices must be carefully handcrafted(More)
We propose a new family of market-based distributed planning algorithms for collaborative multi-agent systems with complex shared constraints. Such constraints tightly couple the agents together, and appear in problems ranging from task or resource allocation to collision avoidance. While it is not immediately obvious, a wide variety of constraints can in(More)
Market-based algorithms have become popular in collaborative multi-agent planning due to their simplicity, distributedness, low communication requirements, and proven success in domains such as task allocation and robotic exploration. Most existing marketbased algorithms, however, suffer from two main drawbacks: resource prices must be carefully handcrafted(More)
For convex games, connections between playing by no-regret algorithms and playing equilibrium strategies have previously been made for Φregret, a generalization of external regret [5]. In particular, Gordon et al. present a no-Φ-regret algorithm for several different classes of transformations Φ [4]. In this paper, we instantiate the algorithm for the class(More)
We study optimization for collaborative multi-agent planning in factored Markov decision processes (MDPs) with shared resource constraints. Following past research, we derive a distributed planning algorithm for this setting based on Lagrangian relaxation: we optimize a convex dual function which maps a vector of resource prices to a bound on the achievable(More)