NIPS Proceedingsβ

Masashi Sugiyama

30 Papers

  • Binary Classification from Positive-Confidence Data (2018)
  • Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces (2018)
  • Co-teaching: Robust training of deep neural networks with extremely noisy labels (2018)
  • Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks (2018)
  • Masking: A New Perspective of Noisy Supervision (2018)
  • Uplift Modeling from Separate Labels (2018)
  • Expectation Propagation for t-Exponential Family Using q-Algebra (2017)
  • Generative Local Metric Learning for Kernel Regression (2017)
  • Learning from Complementary Labels (2017)
  • Positive-Unlabeled Learning with Non-Negative Risk Estimator (2017)
  • Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning (2016)
  • Analysis of Learning from Positive and Unlabeled Data (2014)
  • Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP (2014)
  • Multitask learning meets tensor factorization: task imputation via convex optimization (2014)
  • Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering (2013)
  • Parametric Task Learning (2013)
  • Density-Difference Estimation (2012)
  • Perfect Dimensionality Recovery by Variational Bayesian PCA (2012)
  • Analysis and Improvement of Policy Gradient Estimation (2011)
  • Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent (2011)
  • Relative Density-Ratio Estimation for Robust Distribution Comparison (2011)
  • Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification (2011)
  • Global Analytic Solution for Variational Bayesian Matrix Factorization (2010)
  • Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection (2008)
  • Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation (2007)
  • Multi-Task Learning via Conic Programming (2007)
  • Mixture Regression for Covariate Shift (2006)
  • Active Learning for Misspecified Models (2005)
  • Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction (2005)
  • Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks (1999)
  • 2 Books

  • Advances in Neural Information Processing Systems 29 (2016)
  • Advances in Neural Information Processing Systems 28 (2015)