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Maximum Margin Clustering
TLDR
A new method for clustering based on finding maximum margin hyperplanes through data that leads naturally to a semi-supervised training method for support vector machines by maximizing the margin simultaneously on labeled and unlabeled training data.
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Reward Augmented Maximum Likelihood for Neural Structured Prediction
TLDR
This paper presents a simple and computationally efficient approach to incorporate task reward into a maximum likelihood framework, and shows that an optimal regularized expected reward is achieved when the conditional distribution of the outputs given the inputs is proportional to their exponentiated scaled rewards.
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Probabilities for SV Machines
This chapter contains sections titled: Introduction, Fitting a Sigmoid After the SVM, Empirical Tests, Conclusions, Appendix: Pseudo-code for the Sigmoid Training
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Bridging the Gap Between Value and Policy Based Reinforcement Learning
TLDR
A new RL algorithm, Path Consistency Learning (PCL), is developed that minimizes a notion of soft consistency error along multi-step action sequences extracted from both on- and off-policy traces and significantly outperforms strong actor-critic and Q-learning baselines across several benchmarks.
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Boosting in the Limit: Maximizing the Margin of Learned Ensembles
TLDR
The crucial question as to why boosting works so well in practice, and how to further improve upon it, remains mostly open, and it is concluded that no simple version of the minimum-margin story can be complete.
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Augmenting Naive Bayes Classifiers with Statistical Language Models
TLDR
This paper introduces CAN models, a generalized naive Bayes classifier which allows for a local Markov dependence among observations, and systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses.
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Discriminative Batch Mode Active Learning
TLDR
A discriminative batch mode active learning approach that formulates the instance selection task as a continuous optimization problem over auxiliary instance selection variables to maximize the discrim inative classification performance of the target classifier, while also taking the unlabeled data into account.
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Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled…
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Constraint-based optimization and utility elicitation using the minimax decision criterion
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General Convergence Results for Linear Discriminant Updates
TLDR
A single proof of convergence is given that covers a broad subset of algorithms in this class of “quasi-additive” algorithms, including both Perceptron and Winnow, but also many new algorithms, and demonstrates that the unifying principles are more broadly applicable.
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