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VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Learning and Inference in Hilbert Space with Quantum Graphical Models
Siddarth Srinivasan, Carlton Downey, Byron Boots
Neural Information Processing Systems http://nips.cc/
Near-OptimalCorrelationClusteringwithPrivacy
Then the correlation clustering objective asks to minimize the sum of (the weights of) positive edges across clusters plus the sum of (the weights) of negative edges within clusters.
Multi-Class Learning: From Theory to Algorithm
Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang
Moreover,the proposed multi-class kernel learning algorithms have statistical guarantees and fast convergence rates. Experimental results on lots of benchmark datasets show that our proposed methods can significantly outperform the existing multi-class classification methods. The major contributions ofthispaper include: 1)Anewlocal Rademacher complexitybased bound withfastconvergence rate for multi-class classification is established. Existing works [16,27] for multi-class classifiers with Rademacher complexity does not take into account couplings among different classes.
Meta-Curvature
Eunbyung Park, Junier B. Oliva
We proposemeta-curvature(MC), a framework to learn curvature information for better generalization and fast model adaptation.