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A theory of learning from different domains
TLDR
A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class.
Determinantal Point Processes for Machine Learning
TLDR
Determinantal Point Processes for Machine Learning provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and shows how they can be applied to real-world applications.
Adaptive regularization of weight vectors
TLDR
Empirical evaluations show that AROW achieves state-of-the-art performance on a wide range of binary and multiclass tasks, as well as robustness in the face of non-separable data.
Confidence Estimation for Machine Translation
TLDR
A detailed study of confidence estimation for machine translation, using data from the NIST 2003 Chinese-to-English MT evaluation to investigate various methods for determining whether MT output is correct.
k-DPPs: Fixed-Size Determinantal Point Processes
TLDR
The k-DPP is proposed, a conditional DPP that models only sets of cardinality k, and offers greater expressiveness and control over content, and simplified integration into applications like search.
Learning Bounds for Domain Adaptation
TLDR
Uniform convergence bounds are given for algorithms that minimize a convex combination of source and target empirical risk in order to adapt a classifier from a source domain with a large amount of training data to different target domain with very little training data.
Learning Determinantal Point Processes
TLDR
This thesis shows how determinantal point processes can be used as probabilistic models for binary structured problems characterized by global, negative interactions, and demonstrates experimentally that the techniques introduced allow DPPs to be used for real-world tasks like document summarization, multiple human pose estimation, search diversification, and the threading of large document collections.
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization
TLDR
A corpus of summaries produced by several state-of-the-art extractive summarization systems or by popular baseline systems is presented to facilitate future research on generic summarization and motivates the need for development of more sensitive evaluation measures and for approaches to system combination in summarization.
Near-Optimal MAP Inference for Determinantal Point Processes
TLDR
This paper obtains a practical algorithm with a 1/4-approximation guarantee for a more general class of non-monotone DPPs and extends to MAP inference under complex polytope constraints, making it possible to combine D PPs with Markov random fields, weighted matchings, and other models.
Structured Determinantal Point Processes
TLDR
This model is a marriage of structured probabilistic models, like Markov random fields and context free grammars, with determinantal point processes, which arise in quantum physics as models of particles with repulsive interactions to handle an exponentially-sized set of particles.
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