• Publications
  • Influence
Boosting the margin: A new explanation for the effectiveness of voting methods
TLDR
We analyze the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. Expand
Building text classifiers using positive and unlabeled examples
TLDR
This paper studies the problem of building two-class classifiers with only positive and unlabeled examples, but no negative examples. Expand
SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces
TLDR
We have developed a new point-based POMDP algorithm that exploits the notion of optimally reachable belief spaces to improve com- putational efficiency. Expand
Question classification using support vector machines
TLDR
We propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. Expand
Partially Supervised Classification of Text Documents
TLDR
We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents, identify the documents from class P in M . Expand
An Unsupervised Neural Attention Model for Aspect Extraction
TLDR
We present a novel neural approach to tackle the weaknesses of LDA-based methods for aspect extraction. Expand
DESPOT: Online POMDP Planning with Regularization
TLDR
POMDPs provide a principled framework for planning under uncertainty, but are computationally intractable, due to the "Curse of dimensionality" and the "curse of history". Expand
Planning under Uncertainty for Robotic Tasks with Mixed Observability
TLDR
We use a factored model to represent separately the fully and partially observable components of a robot’s state and derive a compact lower-dimensional representation of its belief space. Expand
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
TLDR
We transform the problem into a problem of learning with noise by labeling all unlabeled examples as negative and use a linear function to learn from the noisy examples. Expand
Convolutional Sequence to Sequence Model for Human Dynamics
TLDR
We present a novel approach to human motion modeling based on convolutional neural networks (CNN). Expand
...
1
2
3
4
5
...