Oral Presentation
  • Chen-Wei Hung and Hsuan-Tien Lin, "Multi-label Active Learning with Auxiliary Learner."
  • Sainbayar Sukhbaatar, Takaki Makino, Kazuyuki Aihara and Takashi Chikayama, "Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Net."
  • Janardhan Rao Doppa, Shahed Sorower, Mohammad NasrEsfahani, Walker Orr, Thomas G. Dietterich, Xiaoli Fern, Prasad Tadepalli and Jed Irvine, "Learning Rules from Incomplete Examples via Implicit Mention Models."
  • Olivier Teytaud and Mario Milone, "Distance-based Rapid Action Value Estimates."
  • Makoto Yamada, Gang Niu, Jun Takagi and Masashi Sugiyama, "Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information."
  • Lizhong Ding and Shizhong Liao, "Approximate Model Selection for Large Scale LSSVM."
  • Chun-Sung Ferng and Hsuan-Tien Lin, "Multi-label Classification with Error-correcting Codes."
  • Akihiro Koide, Kazumi Saito, Kouzou Ohara, Masahiro Kimura and Hiroshi Motoda, "Change-Points Detection of Information Diffusion in Social Network for AsIC-SIS Model."
  • Yuki Yamagishi, Kazumi Saito, Kouzou Ohara, Masahiro Kimura and Hiroshi Motoda, "Learning Attribute-weighted Voter Model over Social Networks."
  • Jing He and Decheng Dai, "Summarization of Yes/No Questions Using a Feature Function Model."
  • Francesco Dinuzzo, Kenji Fukumizu, "Learning low-rank output kernels."
  • Mingmin Chi and Huijun He, "Nonlinear Online Classification Algorithm with Probability Margin."
  • Raphael Bailly, "Quadratic Weighted Automata: Spectral Algorithm and Likelihood Maximization."
  • Jervis Pinto, Alan Fern, Tim Bauer and Martin Erwig, "Improving Policy Gradient Estimates with Independence Information."
  • Masanori Kawakita, Ryota Izumi, Jun'ichi Takeuchi, Yi Hu, Tetsuya Takamori and Hirokazu Kameyama, "Acceleration technique for boosting classification and its application to face detection."
  • Shukai Li and Ivor Tsang, "Learning to Locate Relative Outliers."
  • Jialei Wang, Jinfeng Zhuang and Steven Hoi, "Unsupervised Multiple Kernel Learning."
  • Masakazu Ishihata and Taisuke Sato, "Bayesian inference for statistical abduction using Markov chain Monte Carlo."
  • Blaine Nelson, Battista Biggio and Pavel Laskov, "Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers."
  • Truyen Tran, Dinh Phung and Svetha Venkatesh, "Mixed-Variate Restricted Boltzmann Machines"
  • Battista Biggio, Blaine Nelson and Pavel Laskov, "Support Vector Machines Under Adversarial Label Noise."
  • Kun Zhang and Aapo Hyvarinen, "A general linear non-Gaussian state-space model: Identifiability, identification, and applications."
  • Kilho Shin, "Mapping kernels defined over countably infinite mapping systems and their applications."

Poster Presentation
  • Fu Chang and Chan-Cheng Liu, "Solving Large-Scale Multi-Label SVM Problems with A Tree Decomposition Approach."
  • Olivier Teytaud, Quentin Louveaux and David Lupin St Pierre, "Online sparsity in bandits."
  • Yong Liu and Shizhong Liao, "Accurate Error Bounds for Eigenvalues of Graph Laplacian Matrix and Operator."
  • Alam Ashad and Kenji Fukumizu, "Kernel and Feature Search in Kernel PCA."
  • Akinori Fujino, Masaaki Nagata and Tomoharu Iwata, "Mislabeling Detection with Cross-validation Likelihood Maximization."
  • Muddassar Sindhu and Karl Meinke, "Correctness and Performance of an Incremental Learning Algorithm for Finite Automata."