Masashi Sugiyama

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018)

Masashi Sugiyama, Yung-Kyun Noh, 2019, Machine Learning.

Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching

Masashi Sugiyama, Marthinus Christoffel du Plessis, Masashi Sugiyama, 2012, ICML.

Whitening-Free Least-Squares Non-Gaussian Component Analysis

Gang Niu, Masashi Sugiyama, Hiroaki Sasaki, 2017, ACML.

Parametric Task Learning

Shinichi Nakajima, Masashi Sugiyama, Ichiro Takeuchi, 2013, NIPS.

Generative Local Metric Learning for Kernel Regression

Kee-Eung Kim, Frank Chongwoo Park, Masashi Sugiyama, 2017, NIPS.

Where is the Bottleneck of Adversarial Learning with Unlabeled Data? pdf

Gang Niu, Masashi Sugiyama, Bo Han, 2019, ArXiv.

Active Learning with Model Selection — Simultaneous Optimization of Sample Points and Models for Trigonometric Polynomial Models

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2003 .

Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering

Masashi Sugiyama, Ildefons Magrans de Abril, 2013, IEICE Trans. Inf. Syst..

Integration of Multiple Networks for Robust Label Propagation

Masashi Sugiyama, Hisashi Kashima, Tsuyoshi Kato, 2008, SDM.

A Semi-Supervised Approach to Perceived Age Prediction from Face Images

Masashi Sugiyama, Kazuya Ueki, Yasuyuki Ihara, 2010, IEICE Trans. Inf. Syst..

On the Margin Explanation of Boosting Algorithms

Zhi-Hua Zhou, Liwei Wang, Masashi Sugiyama, 2008, COLT.

Introduction to Dataset Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Mixture Regression for Covariate Shift

Masashi Sugiyama, Amos J. Storkey, A. Storkey, 2006, NIPS.

Online Direct Density-Ratio Estimation Applied to Inlier-Based Outlier Detection

Masashi Sugiyama, Marthinus Christoffel du Plessis, Hiroaki Shiino, 2015, Neural Computation.

Density Ratio Hidden Markov Models pdf

Masashi Sugiyama, John A. Quinn, 2013, ArXiv.

Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization pdf

Masashi Sugiyama, Voot Tangkaratt, Takayuki Osa, 2019, ICLR.

Observational Reinforcement Learning

Masashi Sugiyama, Hirotaka Hachiya, Jaak Simm, 2009 .

Influence-based collaborative active learning

Masashi Sugiyama, Neil Rubens, Masashi Sugiyama, 2007, RecSys '07.

A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme pdf

Masashi Sugiyama, Paavo Parmas, Masashi Sugiyama, 2019, ArXiv.

Positive-Unlabeled Learning with Non-Negative Risk Estimator pdf

Gang Niu, Masashi Sugiyama, Marthinus Christoffel du Plessis, 2017, NIPS.

Efficient Reuse of Previous Experiences to Improve Policies in Real Environment pdf

Jun Morimoto, Masashi Sugiyama, Norikazu Sugimoto, 2014, ArXiv.

On the Training/Test Distributions Gap: A Data Representation Learning Framework

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation pdf

Masashi Sugiyama, Nontawat Charoenphakdee, Jongyeong Lee, 2019, ArXiv.

Fast and Robust Rank Aggregation against Model Misspecification pdf

Ivor W. Tsang, Gang Niu, Masashi Sugiyama, 2019, ArXiv.

Asymptotic Bayesian generalization error when training and test distributions are different

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2007, ICML '07.

A Density-ratio Framework for Statistical Data Processing

Takafumi Kanamori, Liwei Wang, Masashi Sugiyama, 2009, IPSJ Trans. Comput. Vis. Appl..

Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction

Shinichi Nakajima, Masashi Sugiyama, Tsuyoshi Idé, 2008, PAKDD.

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

Masashi Sugiyama, Taiji Suzuki, John A. Quinn, 2013, Neural Computation.

Statistical Reinforcement Learning - Modern Machine Learning Approaches

Masashi Sugiyama, Masashi Sugiyama, 2015, Chapman and Hall / CRC machine learning and pattern recognition series.

Cross-Domain Object Matching with Model Selection

Masashi Sugiyama, Makoto Yamada, 2011, AISTATS.

Statistical outlier detection using direct density ratio estimation

Takafumi Kanamori, Masashi Sugiyama, Hisashi Kashima, 2011, Knowledge and Information Systems.

A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference

Masashi Sugiyama, Hisashi Kashima, Tsuyoshi Kato, 2010, Int. J. Knowl. Discov. Bioinform..

Analysis of Variational Bayesian Matrix Factorization

Shinichi Nakajima, Masashi Sugiyama, 2009, PAKDD.

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence pdf

Mark W. Schmidt, Mohammad Emtiyaz Khan, Masashi Sugiyama, 2015, ArXiv.

Salient Object Detection Based on Direct Density-ratio Estimation

Masashi Sugiyama, Masakazu Matsugu, Masao Yamanaka, 2012 .

Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression

Masashi Sugiyama, Nicole Krämer, Mikio L. Braun, 2009, AISTATS.

Automatic audio tag classification via semi-supervised canonical density estimation

Hirokazu Kameoka, Masashi Sugiyama, Makoto Yamada, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP

Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama, 2014, NIPS.

Bayesian Posterior Approximation via Greedy Particle Optimization

Masashi Sugiyama, Issei Sato, Zhenghang Cui, 2018, AAAI.

A New Meta-Criterion for Regularized Subspace Information Criterion

Masashi Sugiyama, Yasushi Hidaka, Masashi Sugiyama, 2007, IEICE Trans. Inf. Syst..

Density Ratio Estimation: A New Versatile Tool for Machine Learning

Masashi Sugiyama, Masashi Sugiyama, 2009, ACML.

How does Disagreement Help Generalization against Label Corruption?

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2019, ICML.

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric

Masashi Sugiyama, Wonyoung Kim, Myunghee Cho Paik, 2019, Machine Learning.

Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach

Yoshihiro Yamanishi, Masashi Sugiyama, Hisashi Kashima, 2009, Bioinform..

Sequential change‐point detection based on direct density‐ratio estimation

Masashi Sugiyama, Yoshinobu Kawahara, Masashi Sugiyama, 2012, Stat. Anal. Data Min..

Generalization Error Estimation for Non-linear Learning Methods

Masashi Sugiyama, Masashi Sugiyama, 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Learning Under Covariate Shift

Motoaki Kawanabe, Masashi Sugiyama, 2012 .

VILD: Variational Imitation Learning with Diverse-quality Demonstrations pdf

Mohammad Emtiyaz Khan, Masashi Sugiyama, Bo Han, 2019, ICML.

Incremental projection learning for optimal generalization

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2001, Neural Networks.

Dimensionality Reduction of Partially Labeled Multimodal Data

Masashi Sugiyama, Yasushi Kitamura, 2007 .

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Gang Niu, Masashi Sugiyama, Mohan Kankanhalli, 2020, ICML.

High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

Masashi Sugiyama, Eric P. Xing, Makoto Yamada, 2012, Neural Computation.

Information-maximization clustering: analytic solution and model selection (情報論的学習理論と機械学習)

Masashi Sugiyama, Makoto Yamada, Hirotaka Hachiya, 2011 .

Classification from Positive, Unlabeled and Biased Negative Data

Gang Niu, Masashi Sugiyama, Yu-Guan Hsieh, 2018, ICML.

Information-Maximization Clustering Based on Squared-Loss Mutual Information

Gang Niu, Masashi Sugiyama, Makoto Yamada, 2014, Neural Computation.

Adaptive importance sampling for value function approximation in off-policy reinforcement learning

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2009, Neural Networks.

Squared-loss Mutual Information Regularization (情報論的学習理論と機械学習)

Gang Niu, Bo Dai, Masashi Sugiyama, 2012 .

Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios

Takafumi Kanamori, Masashi Sugiyama, Hiroaki Sasaki, 2017, AISTATS.

Chapter 24 – Sparse Regression

Masashi Sugiyama, 2016 .

Parametric Return Density Estimation for Reinforcement Learning pdf

Masashi Sugiyama, Hisashi Kashima, Toshiyuki Tanaka, 2010, UAI.

Unsupervised Discrete Representation Learning

Masashi Sugiyama, Weihua Hu, Takeru Miyato, 2019, Explainable AI.

Perceived Age Estimation from Face Images

Masashi Sugiyama, Yasuyuki Ihara, Kazuya Ueki, 2011 .

Neural Nets WIRN Vietri-98

Masashi Sugiyama, Hidemitsu Ogawa, Sethu Vijayakumar, 1998 .

Variational Bayesian sparse additive matrix factorization

Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, 2013, Machine Learning.

Robust supervised learning under uncertainty in dataset shift pdf

Masashi Sugiyama, Issei Sato, Weihua Hu, 2016, ArXiv.

Support vector comparison machine (情報論的学習理論と機械学習)

Masashi Sugiyama, Toby Dylan Hocking, Supaporn Spanurattana, 2013 .

Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method pdf

Takeaki Uno, Masashi Sugiyama, Koji Tsuda, 2009, ArXiv.

When Training and Test Sets Are Different: Characterizing Learning Transfer

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

New Tricks for Estimating Gradients of Expectations pdf

Richard Nock, Masashi Sugiyama, Cheng Soon Ong, 2019, ArXiv.

Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge pdf

Masashi Sugiyama, Issei Sato, Zijian Xu, 2019, ArXiv.

Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation

Jun Morimoto, Masashi Sugiyama, Voot Tangkaratt, 2013, Neural Networks.

$f$ -Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEEE Transactions on Information Theory.

Geodesic Gaussian kernels for value function approximation

Masashi Sugiyama, Sethu Vijayakumar, Hirotaka Hachiya, 2008, Auton. Robots.

Introduction: special issue of selected papers of ACML 2013

Geoffrey I. Webb, Masashi Sugiyama, Cheng Soon Ong, 2015, Machine Learning.

Online Markov decision processes with policy iteration (情報論的学習理論と機械学習情報論的学習理論ワークショップ(IBIS2015))

Masashi Sugiyama, Hao Zhang, Yao Ma, 2015 .

Designing various component analysis at will

Hirokazu Kameoka, Masashi Sugiyama, Hitoshi Sakano, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Binary Classification from Positive Data with Skewed Confidence pdf

Masashi Sugiyama, Hirotaka Kaji, Kazuhiko Shinoda, 2020, IJCAI.

Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2010, Neural Computation.

Minimum dependency key frames selection via quadratic mutual information

Masashi Sugiyama, Janya Sainui, Masashi Sugiyama, 2015, 2015 Tenth International Conference on Digital Information Management (ICDIM).

Unsupervised Domain Adaptation Based on Source-guided Discrepancy pdf

Masashi Sugiyama, Junya Honda, Han Bao, 2018, AAAI.

Designing Kernel Functions Using the Karhunen-Lo`eve Expansion

Masashi Sugiyama, Hidemitsu Ogawa, 2004 .

Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation

Gang Niu, Masashi Sugiyama, Tingting Zhao, 2015, ACML.

Reward Weighted Regression with Sample Reuse

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2011 .

Covariate Shift Adaptation by Importance Weighted Cross Validation

Klaus-Robert Müller, Masashi Sugiyama, Matthias Krauledat, 2007, J. Mach. Learn. Res..

Statistical Analysis of Distance Estimators with Density Differences and Density Ratios

Takafumi Kanamori, Masashi Sugiyama, Masashi Sugiyama, 2014, Entropy.

Applications of Covariate Shift Adaptation

Motoaki Kawanabe, Masashi Sugiyama, 2012 .

Learning Efficient Tensor Representations with Ring-structured Networks

Masashi Sugiyama, Andrzej Cichocki, Qibin Zhao, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Spectral Methods for Thesaurus Construction

Hiroshi Nakagawa, Masashi Sugiyama, Nobuyuki Shimizu, 2010, IEICE Trans. Inf. Syst..

Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries

Masashi Sugiyama, Hisashi Kashima, Naonori Ueda, 2015, KDD.

Inlier-Based Outlier Detection via Direct Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Hisashi Kashima, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Local Fisher discriminant analysis for supervised dimensionality reduction

Masashi Sugiyama, Masashi Sugiyama, 2006, ICML.

Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2012, AISTATS.

A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin

Zhi-Hua Zhou, Liwei Wang, Masashi Sugiyama, 2011, J. Mach. Learn. Res..

Zero-shot Domain Adaptation Based on Attribute Information pdf

Masashi Sugiyama, Masato Ishii, Takashi Takenouchi, 2019, ACML.

Least-squares two-sample test

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2011, Neural Networks.

Learning from Complementary Labels

Gang Niu, Masashi Sugiyama, Takashi Ishida, 2017, NIPS.

Value Function Approximation on Non-Linear Manifolds for Robot Motor Control

Masashi Sugiyama, Sethu Vijayakumar, Hirotaka Hachiya, 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2016, NIPS.

Incremental projection learning in the presence of noise

Masashi Sugiyama, Hidemitsu Ogawa, 1998 .

Multitask learning meets tensor factorization: task imputation via convex optimization

Masashi Sugiyama, Ryota Tomioka, Kishan Wimalawarne, 2014, NIPS.

Semi-supervised local Fisher discriminant analysis for dimensionality reduction

Shinichi Nakajima, Masashi Sugiyama, Tsuyoshi Idé, 2009, Machine Learning.

Dependence-Maximization Clustering with Least-Squares Mutual Information

Masashi Sugiyama, Manabu Kimura, 2011, J. Adv. Comput. Intell. Intell. Informatics.

Direct conditional probability density estimation with sparse feature selection

Masashi Sugiyama, Voot Tangkaratt, Motoki Shiga, 2014, Machine Learning.

Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative pdf

Gang Niu, Miao Xu, Masashi Sugiyama, 2019, ArXiv.

Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation

Masashi Sugiyama, Kazuya Ueki, Yasuyuki Ihara, 2010, 2010 20th International Conference on Pattern Recognition.

Chapter 33 – Semisupervised Learning

Masashi Sugiyama, 2016 .

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

Masashi Sugiyama, John A. Quinn, Michael U. Gutmann, 2013, ECML/PKDD.

Modal Regression via Direct Log-Density Derivative Estimation

Masashi Sugiyama, Hiroaki Sasaki, Yurina Ono, 2016, ICONIP.

Perfect Dimensionality Recovery by Variational Bayesian PCA

Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka, 2012, NIPS.

Density Ratio Estimation in Machine Learning: Preface

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Trajectory Regression on Road Networks

Masashi Sugiyama, Tsuyoshi Idé, Masashi Sugiyama, 2011, AAAI.

A Density-Ratio Approach to Machine Learning

Masashi Sugiyama, 2011 .

Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios

Gang Niu, Takafumi Kanamori, Aapo Hyvärinen, 2017, J. Mach. Learn. Res..

A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule

Masashi Sugiyama, Marko V. Jankovic, Masashi Sugiyama, 2008, IEEE Signal Processing Letters.

Introduction to Statistical Machine Learning

Masashi Sugiyama, Masashi Sugiyama, 2015 .

In Search of Non-Gaussian Components of a High-Dimensional Distribution

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, J. Mach. Learn. Res..

Learning Common Features of Parametrized Tasks

Shinichi Nakajima, Masashi Sugiyama, Ichiro Takeuchi, 2013 .

Direct Density Derivative Estimation

Gang Niu, Masashi Sugiyama, Hiroaki Sasaki, 2016, Neural Computation.

Online Markov decision processes with policy iteration pdf

Masashi Sugiyama, Hao Zhang, Yao Ma, 2015, ArXiv.

Frank-Wolfe Stein Sampling pdf

Masashi Sugiyama, Issei Sato, Zhenghang Cui, 2018, ArXiv.

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection

Takafumi Kanamori, Masashi Sugiyama, Shohei Hido, 2008, NIPS.

Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration

Jun Morimoto, Masashi Sugiyama, Voot Tangkaratt, 2012, Neural Computation.

Direct importance estimation with probabilistic principal component analyzers

Masashi Sugiyama, Makoto Yamada, Gordon Wichern, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2005, NIPS.

An Online Policy Gradient Algorithm for Continuous State and Action Markov Decision Processes with Bandit Feedback (情報論的学習理論と機械学習情報論的学習理論ワークショップ)

Masashi Sugiyama, Yao Ma, Masashi Sugiyama, 2014 .

Chapter 21 – Learning Models

Masashi Sugiyama, 2016 .

Statistical Machine Learning

Masashi Sugiyama, Masashi Sugiyama, 2016 .

Introduction to the Special Section on the 2nd Asia Conference on Machine Learning (ACML 2010)

Qiang Yang, Masashi Sugiyama, Masashi Sugiyama, 2012, TIST.

Methods of cross-domain object matching (情報論的学習理論と機械学習)

Masashi Sugiyama, Makoto Yamada, 2010 .

Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation

Masashi Sugiyama, Taiji Suzuki, Ryota Tomioka, 2009, J. Mach. Learn. Res..

Guide Actor-Critic for Continuous Control

Masashi Sugiyama, Voot Tangkaratt, Abbas Abdolmaleki, 2017, ICLR.

Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis

Masashi Sugiyama, Hiroshi Sawada, Makoto Yamada, 2013, Digit. Signal Process..

Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation

Gang Niu, Masashi Sugiyama, Guangquan Zhang, 2019 .

Lung lesion detection in FDG-PET/CT with Gaussian process regression

Masashi Sugiyama, Issei Sato, Naoto Hayashi, 2017, Medical Imaging.

Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction

Masashi Sugiyama, Voot Tangkaratt, Hiroaki Sasaki, 2017, Neural Computation.

High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso pdf

Masashi Sugiyama, Makoto Yamada, Wittawat Jitkrittum, 2012, ArXiv.

Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2007, AAAI.

Classification from Pairwise Similarity and Unlabeled Data

Gang Niu, Masashi Sugiyama, Han Bao, 2018, ICML.

Direct Density Ratio Estimation with Convolutional Neural Networks with Application in Outlier Detection

Masashi Sugiyama, Hyun Ha Nam, Masashi Sugiyama, 2014, IEICE Trans. Inf. Syst..

Uplift Modeling from Separate Labels

Masashi Sugiyama, Florian Yger, Jamal Atif, 2018, NeurIPS.

Analysis and Improvement of Policy Gradient Estimation

Gang Niu, Masashi Sugiyama, Tingting Zhao, 2011, NIPS.

Semi-supervised sufficient dimension reduction under class-prior change

Masashi Sugiyama, Hideko Kawakubo, 2016, 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations

Hirokazu Kameoka, Masashi Sugiyama, Hitoshi Sakano, 2010, 2010 20th International Conference on Pattern Recognition.

Bandit-Based Task Assignment for Heterogeneous Crowdsourcing

Masashi Sugiyama, Hao Zhang, Yao Ma, 2015, Neural Computation.

Active Learning in Black-Box Settings

Masashi Sugiyama, Vera Sheinman, Ryota Tomioka, 2016 .

Appendix: List of Symbols and Abbreviations

Motoaki Kawanabe, Masashi Sugiyama, 2012 .

Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation

Masashi Sugiyama, Hisashi Kashima, Tetsuro Morimura, 2010, IEICE Trans. Inf. Syst..

Theory and Algorithm for Learning with Dissimilarity Functions

Liwei Wang, Masashi Sugiyama, Jufu Feng, 2009, Neural Computation.

Dueling Bandits with Qualitative Feedback pdf

Masashi Sugiyama, Junya Honda, Liyuan Xu, 2019, AAAI.

Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer

Masashi Sugiyama, Issei Sato, Ryosuke Kamesawa, 2018 .

Theoretical Views on Dataset and Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices

Masashi Sugiyama, Hisashi Kashima, Taiji Suzuki, 2010, ICML.

Adaptive importance sampling with automatic model selection in reward weighted regression (ニューロコンピューティング)

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2009 .

Learning Discrete Representations via Information Maximizing Self-Augmented Training pdf

Masashi Sugiyama, Weihua Hu, Takeru Miyato, 2017, ICML.

A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, ICA.

Millionaire: a hint-guided approach for crowdsourcing

Ivor W. Tsang, Qiang Yang, Masashi Sugiyama, 2018, Machine Learning.

Improving importance estimation in pool-based batch active learning for approximate linear regression

Masashi Sugiyama, Nozomi Kurihara, Masashi Sugiyama, 2012, Neural Networks.

Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation

Masashi Sugiyama, Hiroaki Sasaki, Ikko Yamane, 2016, Neural Computation.

Global Analytic Solution for Variational Bayesian Matrix Factorization

Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka, 2010, NIPS.

Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning

Masashi Sugiyama, Tingting Zhao, Feng Tian, 2015, IJCAI.

A New Approach to Machine Learning Based on Density Ratios

Masashi Sugiyama, Masashi Sugiyama, 2010 .

Online Multiclass Classification Based on Prediction Margin for Partial Feedback pdf

Masashi Sugiyama, Issei Sato, Takuo Kaneko, 2019, ArXiv.

Learning from Positive and Unlabeled Data(1)Classifier Training and Theoretical Analysis (情報論的学習理論と機械学習情報論的学習理論ワークショップ)

Gang Niu, Masashi Sugiyama, Marthinus Christoffel du Plessis, 2014 .

Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion

Masashi Sugiyama, Keisuke Sakurai, 2006, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Variational Bayesian Learning Theory

Shinichi Nakajima, Masashi Sugiyama, Kazuho Watanabe, 2019 .

Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers

Masashi Sugiyama, Makoto Yamada, Gordon Wichern, 2010, IEICE Trans. Inf. Syst..

On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion

Masashi Sugiyama, Makoto Yamada, Masashi Sugiyama, 2012, IEICE Trans. Inf. Syst..

Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation

Motoaki Kawanabe, Masashi Sugiyama, M. Kawanabe, 2012, Adaptive computation and machine learning.

Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition

Masashi Sugiyama, Naonori Ueda, Hirotaka Hachiya, 2012, Neurocomputing.

Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances

Masashi Sugiyama, Marthinus Christoffel du Plessis, Masashi Sugiyama, 2013, 2013 Conference on Technologies and Applications of Artificial Intelligence.

Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning pdf

Gang Niu, Masashi Sugiyama, Yoav Goldberg, 2019, ArXiv.

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics pdf

Masashi Sugiyama, Takayuki Osa, Volker Gabler, 2019, ArXiv.

Theoretical and Experimental Analyses of Tensor-Based Regression and Classification

Masashi Sugiyama, Ryota Tomioka, Kishan Wimalawarne, 2015, Neural Computation.

Task selection for bandit-based task assignment in heterogeneous crowdsourcing

Masashi Sugiyama, Hao Zhang, H. Zhang, 2015, 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Condition for perfect dimensionality recovery by variational Bayesian PCA

Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka, 2015, J. Mach. Learn. Res..

Importance-Weighted Cross-Validation for Covariate Shift

Klaus-Robert Müller, Masashi Sugiyama, Benjamin Blankertz, 2006, DAGM-Symposium.

Implicit Regularization in Variational Bayesian Matrix Factorization

Shinichi Nakajima, Masashi Sugiyama, 2010, ICML.

Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error

Masashi Sugiyama, Nontawat Charoenphakdee, Masashi Sugiyama, 2018, SDM.

Co-sampling: Training Robust Networks for Extremely Noisy Supervision pdf

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2018, ArXiv.

A fault detection technique for the steel manufacturing process based on a normal pattern library

Masashi Sugiyama, Yoshinobu Kawahara, Kazuya Asano, 2015 .

Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers

Masashi Sugiyama, Tsuyoshi Kato, Jaak Simm, 2010, IPSJ Trans. Comput. Vis. Appl..

Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems

Masashi Sugiyama, Hirotaka Hachiya, Jaak Simm, 2012, IEICE Trans. Inf. Syst..

Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data pdf

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2016, ArXiv.

ECG-Based Concentration Recognition With Multi-Task Regression

Masashi Sugiyama, Hirotaka Kaji, Hisashi Iizuka, 2019, IEEE Transactions on Biomedical Engineering.

Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning

Masashi Sugiyama, Hirotaka Hachiya, Takayuki Akiyama, 2009, IJCAI.

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2016, ICML.

Machine Learning with Mutual Information and Its Application in Robotics

Kiyoshi Irie, Masahiro Tomono, Masashi Sugiyama, 2015 .

Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors

Klaus-Robert Müller, Masashi Sugiyama, Koji Tsuda, 2002, IEEE Trans. Neural Networks.

Importance-weighted covariance estimation for robust common spatial pattern

Masashi Sugiyama, Florian Yger, Alessandro Balzi, 2014, Pattern Recognit. Lett..

Detection of Activities and Events without Explicit Categorization

Masashi Sugiyama, Masakazu Matsugu, Masao Yamanaka, 2013 .

Applications of Density Ratios in Machine Learning

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Algorithms for Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering

Shinichi Nakajima, Akiko Takeda, Masashi Sugiyama, 2013, NIPS.

Mutual Information Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis

Masashi Sugiyama, Makoto Yamada, 2011, AAAI.

Probabilistic principal component analysis based on JoyStick Probability Selector

Masashi Sugiyama, Marko V. Jankovic, 2009, 2009 International Joint Conference on Neural Networks.

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data pdf

Gang Niu, Masashi Sugiyama, Aditya Krishna Menon, 2018, ICLR.

On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis

Masashi Sugiyama, Masashi Sugiyama, 2009, IEICE Trans. Inf. Syst..

Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds

Tomoya Sakai, Masashi Sugiyama, Hiroaki Sasaki, 2017, AISTATS.

On Symmetric Losses for Learning from Corrupted Labels pdf

Masashi Sugiyama, Nontawat Charoenphakdee, Jongyeong Lee, 2019, ICML.

Classification from Triplet Comparison Data

Masashi Sugiyama, Nontawat Charoenphakdee, Zhenghang Cui, 2020, Neural Computation.

Estimating the error at given test input points for linear regression

Masashi Sugiyama, Masashi Sugiyama, 2004, Neural Networks and Computational Intelligence.

Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization

Naoki Hamada, Ken Kobayashi, Masashi Sugiyama, 2018, AAAI.

Model-based reinforcement learning with dimension reduction

Jun Morimoto, Masashi Sugiyama, Voot Tangkaratt, 2016, Neural Networks.

Few-shot Domain Adaptation by Causal Mechanism Transfer

Masashi Sugiyama, Takeshi Teshima, Issei Sato, 2020, ICML.

Single versus Multiple Sorting in All Pairs Similarity Search

Yasuo Tabei, Takeaki Uno, Masashi Sugiyama, 2010, ACML.

Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning pdf

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2016, ArXiv.

Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2008, IEICE Trans. Inf. Syst..

Least-Squares Independent Component Analysis

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2011, Neural Computation.

Stochastic Divergence Minimization for Biterm Topic Models

Masashi Sugiyama, Issei Sato, Zhenghang Cui, 2018, IEICE Trans. Inf. Syst..

Using the variogram for vector outlier screening: application to feature-based image registration

Frank Preiswerk, Polina Golland, Alexandra J. Golby, 2018, International Journal of Computer Assisted Radiology and Surgery.

Are Anchor Points Really Indispensable in Label-Noise Learning?

Gang Niu, Masashi Sugiyama, Chen Gong, 2019, NeurIPS.

A dependence maximization approach towards street map-based localization

Kiyoshi Irie, Masahiro Tomono, Masashi Sugiyama, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives

Masashi Sugiyama, Andrzej Cichocki, Danilo P. Mandic, 2017, Found. Trends Mach. Learn..

Good arm identification via bandit feedback

Masashi Sugiyama, Junya Honda, Atsuyoshi Nakamura, 2017, Machine Learning.

Policy Search with High-Dimensional Context Variables

Masashi Sugiyama, Voot Tangkaratt, Jan Peters, 2016, AAAI.

Nonparametric Return Distribution Approximation for Reinforcement Learning

Masashi Sugiyama, Hisashi Kashima, Toshiyuki Tanaka, 2010, ICML.

Statistical Reinforcement Learning

Masashi Sugiyama, Masashi Sugiyama, 2015 .

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence

Frank Chongwoo Park, Masashi Sugiyama, Daniel D. Lee, 2018, Neural Computation.

Binary Matrix Completion Using Unobserved Entries pdf

Tomoya Sakai, Masashi Sugiyama, Masayoshi Hayashi, 2018, ArXiv.

An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions

Masashi Sugiyama, Tingting Zhao, Yao Ma, 2014, ECML/PKDD.

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search

Motoaki Kawanabe, Takafumi Kanamori, Masashi Sugiyama, 2011, Neural Networks.

Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback

Masashi Sugiyama, Junya Honda, Liyuan Xu, 2019, Neural Computation.

Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density

Aapo Hyvärinen, Masashi Sugiyama, Hiroaki Sasaki, 2014, ECML/PKDD.

Theoretical Analysis of Bayesian Matrix Factorization

Shinichi Nakajima, Masashi Sugiyama, Masashi Sugiyama, 2011, J. Mach. Learn. Res..

On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution

Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, 2011, ICML.

Constrained Least-Squares Density-Difference Estimation

Takafumi Kanamori, Masashi Sugiyama, Marthinus Christoffel du Plessis, 2013, IEICE Trans. Inf. Syst..

Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction

Masashi Sugiyama, Ryota Tomioka, Ryota Tomioka, 2009, IEEE Signal Processing Letters.

Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines

Masashi Sugiyama, Ichiro Takeuchi, Kohei Ogawa, 2013, ICML.

Support vector comparison machines pdf

Masashi Sugiyama, Toby Hocking, Supaporn Spanurattana, 2014, ArXiv.

Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices pdf

Masashi Sugiyama, Florian Yger, 2015, ArXiv.

Order Retrieval

Masashi Sugiyama, Neil Rubens, Takenobu Tokunaga, 2008, LKR.

Theoretical Analysis of Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation

Masashi Sugiyama, Hisashi Kashima, Steffen Bickel, 2008, SDM.

Active Learning with Model Selection

Motoaki Kawanabe, Masashi Sugiyama, 2012 .

Structure Learning of Partitioned Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2016, ICML.

Towards Mixture Proportion Estimation without Irreducibility pdf

Gang Niu, Yu Yao, Masashi Sugiyama, 2020, ArXiv.

On Computational Issues of

Masashi Sugiyama, 2009 .

On Bayesian Transduction: Implications for the Covariate Shift Problem

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering

Masashi Sugiyama, Janya Sainui, 2013, IEICE Trans. Inf. Syst..

Deep Reinforcement Learning with Relative Entropy Stochastic Search

Masashi Sugiyama, Voot Tangkaratt, Abbas Abdolmaleki, 2017 .

Semisupervised Ordinal Regression Based on Empirical Risk Minimization pdf

Masashi Sugiyama, Issei Sato, Nontawat Charoenphakdee, 2019, Neural Computation.

Notation and Symbols

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Statistical analysis of kernel-based least-squares density-ratio estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012, Machine Learning.

On Learning Sparse Changes of Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2014 .

Variational Inference based on Robust Divergences

Masashi Sugiyama, Issei Sato, Futoshi Futami, 2017, AISTATS.

Uncoupled Regression from Pairwise Comparison Data

Masashi Sugiyama, Niu Gang, Junya Honda, 2019, NeurIPS.

Model Selection Under Covariate Shift

Klaus-Robert Müller, Masashi Sugiyama, K. Müller, 2005, ICANN.

between Probability Distributions: Recent Advances in Machine Learning

Masashi Sugiyama, Masashi Sugiyama, 2013 .

Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information

Masashi Sugiyama, Janya Sainui, 2014, IEICE Trans. Inf. Syst..

Cross-Domain Matching with Squared-Loss Mutual Information

Masashi Sugiyama, Machiko Toyoda, Makoto Yamada, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Dimensionality reduction for density ratio estimation in high-dimensional spaces

Motoaki Kawanabe, Masashi Sugiyama, Pui Ling Chui, 2010, Neural Networks.

Incremental Active Learning for Optimal Generalization

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2000, Neural Computation.

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error

Masashi Sugiyama, Masashi Sugiyama, 2006, J. Mach. Learn. Res..

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification pdf

Masashi Sugiyama, Han Bao, Masashi Sugiyama, 2019, AISTATS.

Multi-task Learning with Least-Squares Probabilistic Classifiers (コンピュータビジョンとイメージメディア(CVIM)) -- (テーマセッション)

Masashi Sugiyama, Tsuyoshi Kato, Jaak Simm, 2010 .

Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags pdf

Tomoya Sakai, Masashi Sugiyama, Han Bao, 2017, ArXiv.

Efficient Sample Reuse in EM-Based Policy Search

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2009, ECML/PKDD.

Chapter 16 – Nonparametric Estimation

Masashi Sugiyama, Masashi Sugiyama, 2016 .

Co-teaching: Robust training of deep neural networks with extremely noisy labels

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2018, NeurIPS.

Semi-supervised Estimation of Perceived Age from Face Images

Masashi Sugiyama, Kazuya Ueki, Yasuyuki Ihara, 2010, VISAPP.

Outlier Path: A Homotopy Algorithm for Robust SVM

Masashi Sugiyama, Ichiro Takeuchi, Shinya Suzumura, 2014, ICML.

Active deep Q-learning with demonstration

Masashi Sugiyama, Hsuan-Tien Lin, Voot Tangkaratt, 2018, Machine Learning.

Variational Inference for Gaussian Processes with Panel Count Data

Masashi Sugiyama, Young Lee, Issei Sato, 2018, UAI.

Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs pdf

Gang Niu, Aapo Hyvärinen, Masashi Sugiyama, 2019, Neural Computation.

Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Chapter 34 – Multitask Learning

Masashi Sugiyama, Masashi Sugiyama, 2016 .

Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning

Masashi Sugiyama, Taiji Suzuki, Ryota Tomioka, 2009 .

Least absolute policy iteration for robust value function approximation

Masashi Sugiyama, Hisashi Kashima, Tetsuro Morimura, 2009, 2009 IEEE International Conference on Robotics and Automation.

Mutual information estimation reveals global associations between stimuli and biological processes

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2009, BMC Bioinformatics.

Maximum Volume Clustering

Gang Niu, Bo Dai, Masashi Sugiyama, 2011, AISTATS.

Fully adaptive algorithm for pure exploration in linear bandits

Masashi Sugiyama, Junya Honda, Liyuan Xu, 2017, 1710.05552.

Learning from Noisy Similar and Dissimilar Data pdf

Masashi Sugiyama, Soham Dan, Han Bao, 2020, ECML/PKDD.

Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning

Gang Niu, Bo Dai, Masashi Sugiyama, 2013, ICML.

Recent Advances and Trends in Large-Scale Kernel Methods

Masashi Sugiyama, Hisashi Kashima, Tsuyoshi Kato, 2009, IEICE Trans. Inf. Syst..

Direct Importance Estimation with Gaussian Mixture Models

Masashi Sugiyama, Makoto Yamada, 2009, IEICE Trans. Inf. Syst..

Semi-supervised speaker identification under covariate shift

Masashi Sugiyama, Makoto Yamada, Tomoko Matsui, 2010, Signal Process..

Bayesian Mixture Models

Masashi Sugiyama, 2016 .

Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization

Gang Niu, Bo Dai, Masashi Sugiyama, 2012, Neural Computation.

Active Feature Acquisition with Supervised Matrix Completion

Gang Niu, Miao Xu, Masashi Sugiyama, 2018, KDD.

Masking: A New Perspective of Noisy Supervision

Ivor W. Tsang, Gang Niu, Masashi Sugiyama, 2018, NeurIPS.

The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces

Klaus-Robert Müller, Masashi Sugiyama, K. Müller, 2003, J. Mach. Learn. Res..

Polynomial-time Algorithms for Combinatorial Pure Exploration with Full-bandit Feedback pdf

Masashi Sugiyama, Junya Honda, Liyuan Xu, 2019, ArXiv.

Active Learning in Recommender Systems

Masashi Sugiyama, Dain Kaplan, Neil Rubens, 2011, Recommender Systems Handbook.

Chapter 17 – Bayesian Inference

Masashi Sugiyama, 2016 .

Dataset Shift in Machine Learning

Neil D. Lawrence, Masashi Sugiyama, Anton Schwaighofer, 2009 .

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities

Masashi Sugiyama, Voot Tangkaratt, Hiroaki Sasaki, 2018, Neural Computation.

Automatic audio tagging using covariate shift adaptation

Masashi Sugiyama, Andreas Spanias, Makoto Yamada, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions

Mark W. Schmidt, Mohammad Emtiyaz Khan, Masashi Sugiyama, 2015, UAI.

Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

Masashi Sugiyama, Masashi Sugiyama, 2007, J. Mach. Learn. Res..

Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study

Masashi Sugiyama, Florian Yger, Fabien Lotte, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization

Masashi Sugiyama, Voot Tangkaratt, Ning Xie, 2015, Neural Computation.

Robust Supervised Learning under Distribution Shift Uncertainty

Masashi Sugiyama, Issei Sato, Weihua Hu, 2016 .

Multi-parametric solution-path algorithm for instance-weighted support vector machines

Masashi Sugiyama, Ichiro Takeuchi, Masayuki Karasuyama, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

Supervised Learning under Covarita Shift

Masashi Sugiyama, Masashi Sugiyama, 2006 .

Model Selection Using a Class of Kernels with an Invariant Metric

Masashi Sugiyama, Akira Tanaka, Hideyuki Imai, 2006, SSPR/SPR.

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels pdf

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2018, ArXiv.

Active Learning for Misspecified Models

Masashi Sugiyama, Masashi Sugiyama, 2005, NIPS.

Active Learning with Model Selection in Linear Regression

Masashi Sugiyama, Neil Rubens, Masashi Sugiyama, 2008, SDM.

Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2013, J. Comput. Sci. Eng..

Continuous Target Shift Adaptation in Supervised Learning

Masashi Sugiyama, Marthinus Christoffel du Plessis, Tuan Duong Nguyen, 2015, ACML.

Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach pdf

Gang Niu, Masashi Sugiyama, Tianyi Zhang, 2020, AISTATS.

Multi-race age estimation based on the combination of multiple classifiers

Masashi Sugiyama, Mitsuhiro Fujita, Kazuya Ueki, 2011, The First Asian Conference on Pattern Recognition.

Confidence Scores Make Instance-dependent Label-noise Learning Possible pdf

Gang Niu, Masashi Sugiyama, Bo Han, 2020, ArXiv.

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

Frank Preiswerk, Polina Golland, Alexandra J. Golby, 2018, MICCAI.

Influence-based active learning for collaborative filtering (「プリファレンスモデリングと推薦システム」及び一般)

Masashi Sugiyama, Neil Rubens, Masashi Sugiyama, 2007 .

ν-support vector machine as conditional value-at-risk minimization

Akiko Takeda, Masashi Sugiyama, Masashi Sugiyama, 2008, ICML '08.

Relative Density-Ratio Estimation for Robust Distribution Comparison

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2011, Neural Computation.

Unsupervised key frame selection using information theory and colour histogram difference

Masashi Sugiyama, Janya Sainui, 2018 .

Feature Selection via L1-Penalized Squared-Loss Mutual Information pdf

Masashi Sugiyama, Wittawat Jitkrittum, Hirotaka Hachiya, 2012, IEICE Trans. Inf. Syst..

Chapter 28 – Probabilistic Classification

Masashi Sugiyama, 2016 .

Multi Task Learning with Positive and Unlabeled Data and its Application to Mental State Prediction

Masashi Sugiyama, Hayato Yamaguchi, Hirotaka Kaji, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Chapter 38 – Outlier Detection

Masashi Sugiyama, Masashi Sugiyama, 2016 .

Coping with Active Learning with Model Selection Dilemma: Minimizing Expected Generalization Error

Masashi Sugiyama, Neil Rubens, Masashi Sugiyama, 2006 .

Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification

Masashi Sugiyama, Ichiro Takeuchi, Masashi Sugiyama, 2011, NIPS.

Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression

Hirokazu Kameoka, Masashi Sugiyama, Hitoshi Sakano, 2013 .

Binary Classification Only from Unlabeled Data by Iterative Unlabeled-unlabeled Classification

Masashi Sugiyama, Hirotaka Kaji, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Density-Difference Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012, Neural Computation.

Learning Only from Relevant Keywords and Unlabeled Documents pdf

Masashi Sugiyama, Nontawat Charoenphakdee, Dittaya Wanvarie, 2019, EMNLP.

Semi-supervised AUC optimization based on positive-unlabeled learning

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2017, Machine Learning.

A Framework of Adaptive Brain Computer Interfaces

Yasuharu Koike, Masashi Sugiyama, Yan Li, 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

Dependence maximization localization: a novel approach to 2D street-map-based robot localization

Kiyoshi Irie, Masahiro Tomono, Masashi Sugiyama, 2016, Adv. Robotics.

Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2008, FSDM.

A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast pdf

Masashi Sugiyama, Issei Sato, Zeke Xie, 2020, ArXiv.

Pool-Based Agnostic Experiment Design in Linear Regression

Shinichi Nakajima, Masashi Sugiyama, Masashi Sugiyama, 2008, ECML/PKDD.

Transductive Learning with Multi-class Volume Approximation pdf

Gang Niu, Bo Dai, Masashi Sugiyama, 2014, ICML.

JMLR Workshop and Conference Proceedings Volume 13: 2nd Asian Conference on Machine Learning (ACML'10)

Peter A. Flach, Qiang Yang, Masashi Sugiyama, 2010 .

Multi-parametric solution-path algorithm for instance-weighted support vector machines

Masashi Sugiyama, Ichiro Takeuchi, Masayuki Karasuyama, 2011, MLSP.

Chapter 31 – Online Learning

Masashi Sugiyama, 2016 .

Least-Squares Independence Test

Masashi Sugiyama, Taiji Suzuki, 2011, IEICE Trans. Inf. Syst..

Augmented Lagrangian Methods for Learning, Selecting, and Combining Features

Masashi Sugiyama, Taiji Suzuki, Ryota Tomioka, 2011 .

Machine Learning with Squared-Loss Mutual Information

Masashi Sugiyama, Masashi Sugiyama, 2012, Entropy.

Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise

Masashi Sugiyama, Makoto Yamada, Masashi Sugiyama, 2010, AAAI.

A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision

Miao Xu, Gang Niu, Masashi Sugiyama, 2019 .

Efficient reuse of previous experiences in humanoid motor learning

Jun Morimoto, Masashi Sugiyama, Norikazu Sugimoto, 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression

Masashi Sugiyama, Jaak Simm, Ildefons Magrans de Abril, 2014, IEICE Trans. Inf. Syst..

Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent

Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, 2011, NIPS.

Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers

Masashi Sugiyama, Hirotaka Hachiya, Hyun Ha Nam, 2013, IEICE Trans. Inf. Syst..

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation pdf

Gang Niu, Masashi Sugiyama, Guangquan Zhang, 2019, ArXiv.

On the Ambiguity of Registration Uncertainty pdf

Frank Preiswerk, Polina Golland, Alexandra J. Golby, 2018, ArXiv.

Semi-supervised information-maximization clustering

Gang Niu, Masashi Sugiyama, Daniele Calandriello, 2014, Neural Networks.

Optimal design of regularization term and regularization parameter by subspace information criterion

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2002, Neural Networks.

Density Ratio Estimation : A Comprehensive Review (Statistical Experiment and Its Related Topics)

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010 .

Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model pdf

Masashi Sugiyama, Issei Sato, Hideaki Imamura, 2018, ICML.

SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations

Hirokazu Kameoka, Masashi Sugiyama, Hitoshi Sakano, 2010, International Conference on Pattern Recognition.

Introduction: special issue of selected papers from ACML 2015

Tie-Yan Liu, Zhi-Hua Zhou, Masashi Sugiyama, 2017, Machine Learning.

Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information pdf

Masashi Sugiyama, Makoto Yamada, Leonid Sigal, 2012, ArXiv.

Reinterpreting the Transformation Posterior in Probabilistic Image Registration pdf

Masashi Sugiyama, Jie Luo, Karteek Popuri, 2016, ArXiv.

Matrix Co-completion for Multi-label Classification with Missing Features and Labels pdf

Ivor W. Tsang, Gang Niu, Miao Xu, 2018, ArXiv.

Hierarchical Policy Search via Return-Weighted Density Estimation

Masashi Sugiyama, Takayuki Osa, Masashi Sugiyama, 2017, AAAI.

Semi-supervised AUC optimization based on positive-unlabeled learning

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2017, Machine Learning.

Detection of activities and events without explicit categorization

Masashi Sugiyama, Masakazu Matsugu, Masao Yamanaka, 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

Incremental Construction of Projection Generalizing Neural Networks

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2002 .

Training Data Selection for Optimal Generalization with Noise Variance Reduction in

Masashi Sugiyama, Sethu Vijayakumar, Hidemitsu Ogawa, 1998 .

Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting

Masashi Sugiyama, Masashi Sugiyama, 2010, IEICE Trans. Inf. Syst..

Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2011, Neural Computation.

Mutual information approximation via maximum likelihood estimation of density ratio

Masashi Sugiyama, Taiji Suzuki, Toshiyuki Tanaka, 2009, 2009 IEEE International Symposium on Information Theory.

Information-Theoretic Representation Learning for Positive-Unlabeled Classification

Gang Niu, Masashi Sugiyama, Tomoya Sakai, 2017, Neural Computation.

Class-prior estimation for learning from positive and unlabeled data

Gang Niu, Masashi Sugiyama, Marthinus Christoffel du Plessis, 2016, Machine Learning.

Sparse Additive Matrix Factorization for Robust PCA and Its Generalization

Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, 2012, ACML.

Lighting Condition Adaptation for Perceived Age Estimation

Masashi Sugiyama, Kazuya Ueki, Yasuyuki Ihara, 2011, IEICE Trans. Inf. Syst..

Global analytic solution of fully-observed variational Bayesian matrix factorization

Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka, 2013, J. Mach. Learn. Res..

Convex Formulation for Learning from Positive and Unlabeled Data

Gang Niu, Masashi Sugiyama, Marthinus Christoffel du Plessis, 2015, ICML.

Does Distributionally Robust Supervised Learning Give Robust Classifiers?

Gang Niu, Masashi Sugiyama, Issei Sato, 2016, ICML.

Constructing Kernel Functions for Binary Regression

Masashi Sugiyama, Hidemitsu Ogawa, 2006, IEICE Trans. Inf. Syst..

Multitask Principal Component Analysis

Masashi Sugiyama, Florian Yger, Maxime Berar, 2016, ACML.

Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information

Masashi Sugiyama, Hirotaka Hachiya, Masashi Sugiyama, 2010, ECML/PKDD.

A batch ensemble approach to active learning with model selection

Masashi Sugiyama, Neil Rubens, Masashi Sugiyama, 2008, Neural Networks.

Output Divergence Criterion for Active Learning in Collaborative Settings

Masashi Sugiyama, Neil Rubens, Ryota Tomioka, 2009 .

Subspace Information Criterion for Model Selection

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2001, Neural Computation.

Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

Masashi Sugiyama, Issei Sato, Yusuke Tsuzuku, 2018, NeurIPS.

Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations

Masashi Sugiyama, Voot Tangkaratt, Masashi Sugiyama, 2018 .

Least-Squares Conditional Density Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEICE Trans. Inf. Syst..

Theoretical and Experimental Evaluation of the Subspace Information Criterion

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2002, Machine Learning.

Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation

Masashi Sugiyama, Hisashi Kashima, Steffen Bickel, 2009, J. Inf. Process..

Pilot Study on Verifying the Monotonic Relationship between Error and Uncertainty in Deformable Registration for Neurosurgery pdf

Alexandra J. Golby, Masashi Sugiyama, William M. Wells, 2019, ArXiv.

Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models

Tomoya Sakai, Masashi Sugiyama, 2014, IEICE Trans. Inf. Syst..

Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

Masashi Sugiyama, Masahiro Yukawa, Mikael Johansson, 2018, NeurIPS.

SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity pdf

Gang Niu, Bo Dai, Masashi Sugiyama, 2011, ArXiv.

Density Ratio Estimation in Machine Learning

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Chapter 30 – Ensemble Learning

Masashi Sugiyama, 2016 .

Subspace Information Criterion for Image Restoration---Optimizing Parameters in Lin

Masashi Sugiyama, Hidemitsu Ogawa, Daisuke Imaizumi, 2001 .

Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

Motoaki Kawanabe, Shinichi Nakajima, Masashi Sugiyama, 2007, NIPS.

Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data pdf

Gang Niu, Tomoya Sakai, Masashi Sugiyama, 2017, ArXiv.

Geometry-aware stationary subspace analysis pdf

Masashi Sugiyama, Florian Yger, Inbal Horev, 2016, ACML.

On Generalization Performance and Non-Convex Optimization of Extended ν-Support Vector Machine

Akiko Takeda, Masashi Sugiyama, 2008, New Generation Computing.

Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes?

Shinichi Nakajima, Masashi Sugiyama, Masashi Sugiyama, 2014, AISTATS.

On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution

Masashi Sugiyama, Makoto Yamada, Hirotaka Hachiya, 2011, ICML.

Binary Classification from Positive-Confidence Data

Gang Niu, Masashi Sugiyama, Takashi Ishida, 2017, NeurIPS.

Misdirected Registration Uncertainty pdf

Masashi Sugiyama, William M. Wells, Jie Luo, 2017, ArXiv.

Discriminative Learning under Covariate Shift with a Single Optimization Problem

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Regularizing generalization error estimators: a novel approach to robust model selection

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2004, ESANN.

Convex formulation of multiple instance learning from positive and unlabeled bags

Tomoya Sakai, Masashi Sugiyama, Han Bao, 2017, Neural Networks.

Geometry-aware principal component analysis for symmetric positive definite matrices

Masashi Sugiyama, Florian Yger, Inbal Horev, 2017, Machine Learning.

Geometry of Covariate Shift with Applications to Active Learning

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Chapter 37 – Clustering

Masashi Sugiyama, Masashi Sugiyama, 2016 .

Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier

Masashi Sugiyama, Tsubasa Kobayashi, Masashi Sugiyama, 2012, IEICE Trans. Inf. Syst..

Properties of incremental projection learning

Masashi Sugiyama, Hidemitsu Ogawa, 2001, Neural Networks.

Expectation Propagation for t-Exponential Family Using q-Algebra

Masashi Sugiyama, Issei Sato, Futoshi Futami, 2017, NIPS.

Improving the Accuracy of Least-Squares Probabilistic Classifiers

Masashi Sugiyama, Makoto Yamada, Gordon Wichern, 2011, IEICE Trans. Inf. Syst..

A Least-squares Approach to Direct Importance Estimation

Takafumi Kanamori, Masashi Sugiyama, Shohei Hido, 2009, J. Mach. Learn. Res..

Pseudo orthogonal bases give the optimal generalization capability in neural network learning

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 1999, Optics & Photonics.

Direct Density Ratio Estimation with Dimensionality Reduction

Motoaki Kawanabe, Takafumi Kanamori, Masashi Sugiyama, 2010, SDM.

Learning from Indirect Observations pdf

Masashi Sugiyama, Nontawat Charoenphakdee, Yivan Zhang, 2019, ArXiv.

Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation pdf

Masashi Sugiyama, Shota Nakajima, 2021, ArXiv.

Mixture Proportion Estimation in Weakly Supervised Learning

Masashi Sugiyama, 2021, CIKM Workshops.

Confidence Scores Make Instance-dependent Label-noise Learning Possible

Gang Niu, Masashi Sugiyama, Tongliang Liu, 2019, ICML.

Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study

Masashi Sugiyama, Hisashi Kashima, Naonori Ueda, 2015, Journal of medical Internet research.

Improving the Accuracy of

Masashi Sugiyama, Makoto Yamada, Gordon Wichern, 2011 .

Binary Classification under Sample Selection Bias

Masashi Sugiyama, Joaquin Quiñonero-Candela, Anton Schwaighofer, 2009 .

Boosting and margin theory

Zhi-Hua Zhou, Masashi Sugiyama, Jufu Feng, 2012 .

Anomaly Detection by Deep Direct Density Ratio Estimation

Masashi Sugiyama, Masahiro Abe, 2019 .

A new algorithm of non-Gaussian component analysis with radial kernel functions

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2007 .

Pumpout: A Meta Approach to Robust Deep Learning with Noisy Labels

Xingrui Yu, Gang Niu, Miao Xu, 2018, 1809.11008.

Bayesian Nonparametric Poisson Process Allocation

Masashi Sugiyama, Issei Sato, Hongyi Ding, 2017 .

Sufficient Component Analysis for Supervised Dimension Reduction

Gang Niu, Masashi Sugiyama, Makoto Yamada, 2011, 1103.4998.

Learning Under Non-stationarity: Covariate Shift Adaptation by Importance Weighting

Masashi Sugiyama, Masashi Sugiyama, 2012 .

Model Selection with Small Samples

Masashi Sugiyama, Hidemitsu Ogawa, H. Ogawa, 2001 .

The Degrees of Freedom of Partial Least Squares Regression

Masashi Sugiyama, Masashi Sugiyama, Nicole Kraemer, 2010, 1002.4112.