Romain Thibaux

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We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple Xbar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals. In contrast with previous work, we are able to split various(More)
We present the Viewpoint Feature Histogram (VFH), a descriptor for 3D point cloud data that encodes geometry and viewpoint. We demonstrate experimentally on a set of 60 objects captured with stereo cameras that VFH can be used as a distinctive signature, allowing simultaneous recognition of the object and its pose. The pose is accurate enough for robot(More)
We present <i>distributed regression</i>, an efficient and general framework for <i>in-network</i> modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon <i>kernel linear regression</i>, where the model takes the form of a(More)
The problem of learning a sparse conic combination of kernel functions or kernel matrices for classification or regression can be achieved via the regularization by a block 1-norm [1]. In this paper, we present an algorithm that computes the entire regularization path for these problems. The path is obtained by using numerical continuation techniques, and(More)
Virtually all existing mobile robot localization techniques operate on a static map of the environment. When the environment changes (e.g., doors are opened or closed), there is an opportunity to simultaneously estimate the robot’s pose and the state of the environment. The resulting estimation problem is high-dimensional, rendering current localization(More)
Nonparametric Bayesian Models for Machine Learning by Romain Jean Thibaux Doctor of Philosophy in Computer Science and the Designated Emphasis in Communication, Computation and Statistics University of California, Berkeley Professor Michael I. Jordan, Chair This thesis presents general techiques for inference in various nonparametric Bayesian models,(More)
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