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Active Learning by Querying Informative and Representative Examples
TLDR
The proposed QUIRE approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance by incorporating the correlation among labels and is extended to multi-label learning by actively querying instance-label pairs.
Trading regret for efficiency: online convex optimization with long term constraints
TLDR
This paper proposes an efficient algorithm which achieves O(√T) regret bound and O(T3/4) bound on the violation of constraints and proposes a multipoint bandit feedback algorithm with the same bounds in expectation as the first algorithm.
SemiBoost: Boosting for Semi-Supervised Learning
TLDR
A boosting framework for semi-supervised learning, termed as SemiBoost, that improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples and is comparable to the state-of-the-art semi- supervised learning algorithms.
Regularized Distance Metric Learning: Theory and Algorithm
TLDR
It is shown that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data.
Online AUC Maximization
TLDR
This work develops online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions, and presents two algorithms for online AUC maximization with theoretic performance guarantee.
Simple and Efficient Multiple Kernel Learning by Group Lasso
TLDR
This paper forms a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL that leads to an efficient algorithm for MKL, but also generalizes to the case for Lp-MKL.
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
TLDR
This paper considers a distributed communication efficient momentum SGD method and proves its linear speedup property, filling the gap in the study of distributed SGD variants with reduced communication.
Batch mode active learning and its application to medical image classification
TLDR
A framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously and is more effective than the state-of-the-art algorithms for active learning.
On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering
TLDR
A systematic analysis of nonnegative matrix factorization (NMF) relating to data cluster- ing and the importance of orthogonality in NMF and soft clustering nature of NMF is emphasized.
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