Session 3: Supervised and General Machine Learning

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Information

Day 3 (Nov.19), talks: 10:50-11:30, poster session: 11:30-14:00

• Each oral presentation consists of a 12-minute talk and a 2-minute discussion (except for best paper presentations).
• Each spotlight presentation consists of 2.5-minute talk.
• Both oral and spotlight presentations are also presented in the poster session.

List of papers

• [Spotlight] [Poster: Tue01] Learning Weighted Top-k Support Vector Machine
  • Tsuyoshi Kato (Gunma University); Yoshihiro Hirohashi (DENSO CORPORATION) [Conference Track, Paper ID: 304]
• [Spotlight] [Poster: Tue02] Multiple Empirical Kernel Learning with Discriminant Locality Preservation
  • Bolu Wang (East China University of Science and Technology); Dongdong Li (East China University of Science and Technology); Zhe Wang ( East China University of Science and Technology ) [Conference Track, Paper ID: 67]
• [Spotlight] [Poster: Tue03] Optimal PAC-Bayesian Posteriors for Stochastic Classifiers and their use for Choice of SVM Regularization Parameter
  • Puja Sahu (Indian Institute of Technology Bombay); Nandyala Hemachandra (Indian Institute of Technology Bombay) [Conference Track, Paper ID: 125]
• [Spotlight] [Poster: Tue04] Zero-shot Domain Adaptation Based on Attribute Information
  • Masato Ishii (The University of Tokyo/RIKEN/NEC); Takashi Takenouchi (Future University Hakodate/RIKEN Center for Advanced Intelligence Project); Masashi Sugiyama (RIKEN/The University of Tokyo) [Conference Track, Paper ID: 237]
• [Spotlight] [Poster: Tue05] Kernel Learning for Data-Driven Spectral Analysis of Koopman Operators
  • Naoya Takeishi (RIKEN) [Conference Track, Paper ID: 352]
• [Spotlight] [Poster: Tue06] Stochastic Gradient Trees
  • Henry Gouk (University of Edinburgh); Bernhard Pfahringer (University of Waikato); Eibe Frank (University of Waikato) [Conference Track, Paper ID: 395]
• [Spotlight] [Poster: Tue07] Minimax Online Prediction of Varying Bernoulli Process under Variational Approximation
  • Kenta Konagayoshi (Kyushu University); Kazuho Watanabe (Toyohashi University of Technology) [Conference Track, Paper ID: 81]
• [Spotlight] [Poster: Tue08] Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA
  • Vitalik Melnikov (Paderborn University); Eyke Hüllermeier (University of Paderborn) [Conference Track, Paper ID: 397]
• [Spotlight] [Poster: Tue09] Learning to Augment with Feature Side-information
  • Amina Mollaysa (university of geneva); Alexandros Kalousis (AU Geneva); Eric Bruno (Expedia); Maurits Diephuis (University of Geneva) [Conference Track, Paper ID: 111]
• [Spotlight] [Poster: Tue10] Self-Paced Multi-Label Learning with Diversity
  • Seyed Amjad Seyedi (University of Kurdistan); S.Siamak Ghodsi (University of Kurdistan); Fardin Akhlaghian Tab (University of Kurdistan); Mahdi Jalili (RMIT University); Parham Moradi (University of Kurdistan) [Conference Track, Paper ID: 316]
• [Spotlight] [Poster: Tue11] FEARS: a FEature And Representation Selection approach for time series classification
  • Alexis Bondu (Orange); Dominique Gay (Université de La Réunion); Vincent Lemaire (Orange); Marc Boulle (Orange Labs); Eole Cervenka (Orange) [Conference Track, Paper ID: 213]
• [Spotlight] [Poster: Tue12] Prediction of Crowd Flow in City Complex with Missing Data
  • Shiyang Qiu (University of Science and Technology of China); Peng Xu (University of Science and Technology of China); Wei Zheng (Kehang Technology and Information); Wang Junjie (University of Science and Technology of China); Guo Yu (China People's Police University); Mingyao Hou (Kehang Technology and Information); Hengchang Liu (USTC) [Conference Track, Paper ID: 298]