Wee Sun Lee

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Reid G. Simmons62
Trey Smith62
Sebastian Thrun56
Leslie Pack Kaelbling54
Thorsten Joachims53
45David Hsu
30Siddhartha S. Srinivasa
29Michael C. Koval
24Peter L. Bartlett
21Tomás Lozano-Pérez

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Boosting the margin: A new explanation for the effectiveness of voting methods
One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the… (More)
SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces
— Motion planning in uncertain and dynamic environments is an essential capability for autonomous robots. Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for solving such problems, but they are often avoided in robotics due to high computational complexity. Our goal is to create practical POMDP algorithms… (More)
Building Text Classifiers Using Positive and Unlabeled Examples
This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each… (More)
Partially Supervised Classification of Text Documents
We investigate the following problem: Given a set of documents of a particular topic or class È , and a large set Å of mixed documents that contains documents from class È and other types of documents, identify the documents from class È in Å. The key feature of this problem is that there is no labeled non-È document, which makes traditional machine… (More)
Planning under Uncertainty for Robotic Tasks with Mixed Observability
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic… (More)
Efficient agnostic learning of neural networks with bounded fan-in
We show that the class of two layer neural networks with bounded fan-in is eeciently learn-able in a realistic extension to the Probably Approximately Correct (PAC) learning model. In this model, a joint probability distribution is assumed to exist on the observations and the learner is required to approximate the neural network which minimizes the expected… (More)
A Generative Model for Parsing Natural Language to Meaning Representations
In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. The model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. We introduce dynamic programming techniques for efficient… (More)
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
The problem of learning with positive and unla-beled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labeling all unla-beled examples as negative and use a linear function to learn from the noisy examples. To learn a linear function with noise, we perform logistic regression after… (More)