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Learning with Local and Global Consistency
TLDR
A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Expand
Recommender systems with social regularization
TLDR
This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods. Expand
Learning with Hypergraphs: Clustering, Classification, and Embedding
TLDR
This paper generalizes the powerful methodology of spectral clustering which originally operates on undirected graphs to hypergraphs, and further develop algorithms for hypergraph embedding and transductive classification on the basis of the spectral hypergraph clustering approach. Expand
Ranking on Data Manifolds
TLDR
A simple universal ranking algorithm for data lying in the Euclidean space, such as text or image data, to rank the data with respect to the intrinsic manifold structure collectively revealed by a great amount of data. Expand
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing
TLDR
Experimental results demonstrate that the proposed algorithm for multi-class crowd labeling problems is comparable to the most accurate empirical approach, while outperforming several other recently proposed methods. Expand
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
TLDR
A new off-policy estimation method that applies importance sampling directly on the stationary state-visitation distributions to avoid the exploding variance issue faced by existing estimators is proposed. Expand
Learning from labeled and unlabeled data on a directed graph
TLDR
A general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered, which generalizes the spectral clustering approach for undirected graphs. Expand
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
TLDR
This work proposes an upper confidence bound based algorithm for generalized linear contextual bandits, which achieves an \tilde{O}(\sqrt{dT}) regret over T rounds with d dimensional feature vectors, and proves it to have optimal regret for the certain cases. Expand
Semi-Supervised Graph-Based Hyperspectral Image Classification
TLDR
The introduction of the composite-kernel framework drastically improves results, and the new fast formulation ranks almost linearly in the computational cost, rather than cubic as in the original method, thus allowing the use of this method in remote-sensing applications. Expand
Learning from the Wisdom of Crowds by Minimax Entropy
TLDR
This work proposes a minimax entropy principle to improve the quality of noisy labels from crowds of nonexperts, and shows that a simple coordinate descent scheme can optimize minimAX entropy. Expand
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