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ICML2011で気になった論文

ICML

適当に選んでたら多すぎたのでもっかいスクリーニングにかける。

  • #38 A Graph-based Framework for Multi-Task Multi-View Learning, Jingrui He; Rick Lawrence (pdf)
  • #93 Large Scale Text Classification using Semi-supervised Multinomial Naive Bayes, Jiang Su; Jelber Sayyad Shirab; Stan Matwin (pdf)
  • #125 Parsing Natural Scenes and Natural Language with Recursive Neural Networks, Richard Socher; Cliff Chiung-Yu Lin; Andrew Ng; Chris Manning (pdf)
  • #135 Learning Mallows Models with Pairwise Preferences, Tyler Lu; Craig Boutilier (pdf)
  • #159 Time Series Clustering: Complex is Simpler!,Lei Li; B. Aditya Prakash (pdf)
  • #175 Inference of Inversion Transduction Grammars, Alexander Clark (pdf)
  • #195 Pruning nearest neighbor cluster trees, Samory Kpotufe; Ulrike von Luxburg (pdf)
  • #205 Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning, Francesco Orabona; Luo Jie (pdf)
  • #210 Exploring optimization strategies for training deep learning, Quoc Le; Jiquan Ngiam; Adam Coates; Andrew Ng (pdf)
  • #232 Large-Scale Convex Minimization with a Low-Rank Constraint, Shai Shalev-Shwartz; Alon Gonen; Ohad Shamir (pdf)
  • #237 Online submodular minimization for combinatorial structures, Stefanie Jegelka; Jeff Bilmes (pdf)
  • #262 Topic Modeling with Nonparametric Markov Tree, Haojun Chen; David Dunson; Lawrence Carin (pdf)
  • #272 A Co-training Approach for Multi-view Spectral Clustering, Abhishek Kumar; Hal Daume III (pdf)
  • #306 Better Algorithms for Selective Sampling, Francesco Orabona; Nicolò Cesa-Bianchi (pdf)
  • #342 Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach, Xavier Glorot; Antoine Bordes; Yoshua Bengio (pdf)
  • #344 Learning with Whom to Share in Multi-task Feature Learning, Zhuoliang Kang; Fei Sha; Kristen Grauman (pdf)
  • #374 Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines, Jun Zhu; Ning Chen; Eric Xing (pdf)
  • #375 On the Integration of Topic Modeling and Dictionary Learning, Lingbo Li; Mingyuan Zhou; Guillermo Sapiro; Lawrence Carin (pdf)
  • #386 Support Vector Machines as Probabilistic Models, Vojtech Franc; Alexander Zien; Bernhard Schölkopf (pdf)
  • #437 Hierarchical Classification via Orthogonal Transfer, Lin Xiao; Dengyong Zhou; Mingrui Wu (pdf)
  • #465 The Constrained Weight Space SVM: Learning with Labeled Features, Kevin Small; Byron Wallace; Carla Brodley; Thomas Trikalinos (pdf)
  • #473 Online Discovery of Feature Dependencies, Alborz Geramifard; Finale Doshi; Joshua Redding; Nicholas Roy ; Jonathan How (pdf)
  • #483 Infinite Dynamic Bayesian Networks, Finale Doshi; David Wingate; Nicholas Roy; Josh Tenenbaum (pdf)
  • #498 Automatic Feature Decomposition for Single View Co-training, Minmin Chen; Kilian Weinberger; Yixin Chen (pdf)
  • #506 Size-constrained Submodular Minimization through Minimum Norm Base, Kiyohito Nagano; Yoshinobu Kawahara; Kazuyuki Aihara (pdf)
  • #508 Locally Linear Support Vector Machines, Lubor Ladicky; Philip Torr (pdf)
  • #524 Generating Text with Recurrent Neural Networks, Ilya Sutskever; James Martens; Geoffrey Hinton (pdf)
  • #542 Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection, Abhimanyu Das; David Kempe (pdf)
  • #545 A Spectral Algorithm for Latent Tree Graphical Models, Ankur Parikh; Le Song; Eric Xing (pdf)
  • #548 Towards Making Unlabeled Data Never Hurt, Yu-Feng Li; Zhi-Hua Zhou (pdf)
  • #551 On Random Weights and Unsupervised Feature Learning, Andrew Saxe; Pang Wei Koh; Zhenghao Chen; Maneesh Bhand; Bipin Suresh; Andrew Ng (pdf)