Marthinus Christoffel du Plessis

发表

Analysis of Learning from Positive and Unlabeled Data

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

Online Direct Density-ratio Estimation under the Kullback-Leibler Loss (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, Marthinus Christoffel du Plessis, Hiroaki Shiino, 2014 .

Computationally Efficient Class-Prior Estimation under Class Balance Change Using Energy Distance

Masashi Sugiyama, Marthinus Christoffel du Plessis, Hideko Kawakubo, 2016, IEICE Trans. Inf. Syst..

Learning from Positive and Unlabeled Data(2)Computationally Efficient Estimation of Class Priors (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 .

Class Prior Estimation from Positive and Unlabeled Data

Masashi Sugiyama, Marthinus Christoffel du Plessis, Masashi Sugiyama, 2014, IEICE Trans. Inf. Syst..

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

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

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

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

Positive-Unlabeled Learning with Non-Negative Risk Estimator pdf

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

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

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

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 .

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.

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

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

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

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

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

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

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

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

Constrained Least-Squares Density-Difference Estimation

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

Coping with Class Balance Change in Classification : Class-Prior Estimation with Energy Distance

Sugiyama Masashi, Marthinus Christoffel du Plessis, Kawakubo Hideko, 2014 .

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.

Density-Difference Estimation

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

Transductive Learning with Multi-class Volume Approximation pdf

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

Class-prior estimation for learning from positive and unlabeled data

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

Convex Formulation for Learning from Positive and Unlabeled Data

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