Learning with Local and Global Consistency
- Dengyong Zhou, O. Bousquet, T. N. Lal, J. Weston, B. Schölkopf
- Computer ScienceNIPS
- 9 December 2003
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.
Recommender systems with social regularization
- Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, Irwin King
- Computer ScienceWeb Search and Data Mining
- 9 February 2011
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.
Learning with Hypergraphs: Clustering, Classification, and Embedding
- Dengyong Zhou, Jiayuan Huang, B. Schölkopf
- Computer ScienceNIPS
- 4 December 2006
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.
Ranking on Data Manifolds
- Dengyong Zhou, J. Weston, A. Gretton, O. Bousquet, B. Schölkopf
- Computer ScienceNIPS
- 9 December 2003
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.
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
- Lihong Li, Yu Lu, Dengyong Zhou
- Computer ScienceInternational Conference on Machine Learning
- 28 February 2017
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.
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
- Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
- MathematicsNeural Information Processing Systems
- 29 October 2018
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.
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing
- Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan
- Computer ScienceJournal of machine learning research
- 15 June 2014
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.
Learning from labeled and unlabeled data on a directed graph
- Dengyong Zhou, Jiayuan Huang, B. Schölkopf
- Computer ScienceInternational Conference on Machine Learning
- 7 August 2005
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.
Evolutionary spectral clustering by incorporating temporal smoothness
- Yun Chi, Xiaodan Song, Dengyong Zhou, K. Hino, B. Tseng
- Computer ScienceKnowledge Discovery and Data Mining
- 12 August 2007
This paper proposes two frameworks that incorporate temporal smoothness in evolutionary spectral clustering and demonstrates that their methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems.
Learning from the Wisdom of Crowds by Minimax Entropy
- Dengyong Zhou, John C. Platt, S. Basu, Yi Mao
- Computer ScienceNIPS
- 3 December 2012
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.
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