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2020 – today
- 2023
[j170]Zhenguo Wu, Jiaqi Lv, Masashi Sugiyama:
Learning With Proper Partial Labels. Neural Comput. 35(1): 58-81 (2023)- 2022
- [j169]Akira Tanimoto
, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima:
Improving imbalanced classification using near-miss instances. Expert Syst. Appl. 201: 117130 (2022) - [j168]Hiroki Ishiguro, Takashi Ishida, Masashi Sugiyama:
Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Trans. Inf. Syst. 105-D(2): 364-376 (2022) - [j167]Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama:
Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. 23: 23:1-23:35 (2022) - [j166]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks 152: 90-104 (2022) - [j165]Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto:
Deep learning, reinforcement learning, and world models. Neural Networks 152: 267-275 (2022) - [j164]Kenji Doya, Karl J. Friston, Masashi Sugiyama, Joshua B. Tenenbaum:
Neural Networks special issue on Artificial Intelligence and Brain Science. Neural Networks 155: 328-329 (2022) - [j163]Chen Gong, Jian Yang, Jane You, Masashi Sugiyama:
Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(6): 2841-2855 (2022) - [j162]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3590-3601 (2022) - [c248]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS 2022: 2618-2640 - [c247]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS 2022: 5051-5083 - [c246]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c245]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c244]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c243]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. ICLR 2022 - [c242]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c241]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c240]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c239]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c238]Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama:
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. ICML 2022: 24430-24459 - [c237]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. ICML 2022: 24743-24769 - [c236]Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan:
Towards Adversarially Robust Deep Image Denoising. IJCAI 2022: 1516-1522 - [i186]Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan:
Towards Adversarially Robust Deep Image Denoising. CoRR abs/2201.04397 (2022) - [i185]Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR abs/2202.00395 (2022) - [i184]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. CoRR abs/2202.03077 (2022) - [i183]Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama:
On the Effectiveness of Adversarial Training against Backdoor Attacks. CoRR abs/2202.10627 (2022) - [i182]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR abs/2204.03304 (2022) - [i181]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal approximation property of invertible neural networks. CoRR abs/2204.07415 (2022) - [i180]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR abs/2206.01606 (2022) - [i179]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i178]Charles Riou, Junya Honda, Masashi Sugiyama:
The Survival Bandit Problem. CoRR abs/2206.03019 (2022) - [i177]Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama:
Learning from Multiple Unlabeled Datasets with Partial Risk Regularization. CoRR abs/2207.01555 (2022) - [i176]Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou:
Adapting to Online Label Shift with Provable Guarantees. CoRR abs/2207.02121 (2022) - [i175]Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama:
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. CoRR abs/2208.02011 (2022) - [i174]Nobutaka Ito, Masashi Sugiyama:
Audio Signal Enhancement with Learning from Positive and Unlabelled Data. CoRR abs/2210.15143 (2022) - [i173]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i172]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning. CoRR abs/2211.13257 (2022) - [i171]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. CoRR abs/2212.13251 (2022) - 2021
- [j161]Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt:
Constraint learning for control tasks with limited duration barrier functions. Autom. 127: 109504 (2021) - [j160]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33(1): 244-268 (2021) - [j159]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33(5): 1234-1268 (2021) - [j158]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. 33(8): 2128-2162 (2021) - [j157]Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama:
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. Neural Comput. 33(8): 2163-2192 (2021) - [j156]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33(12): 3361-3412 (2021) - [j155]Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama:
A One-Step Approach to Covariate Shift Adaptation. SN Comput. Sci. 2(4): 319 (2021) - [c235]Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. AISTATS 2021: 298-306 - [c234]Han Bao, Masashi Sugiyama:
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS 2021: 1648-1656 - [c233]Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama:
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS 2021: 1783-1791 - [c232]Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients. AISTATS 2021: 4078-4086 - [c231]Masashi Sugiyama:
Mixture Proportion Estimation in Weakly Supervised Learning. CIKM Workshops 2021 - [c230]Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama:
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. CVPR 2021: 5202-5211 - [c229]Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama:
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL 2021: 581-592 - [c228]Zeke Xie, Issei Sato, Masashi Sugiyama:
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. ICLR 2021 - [c227]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. ICLR 2021 - [c226]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c225]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. ICML 2021: 1272-1282 - [c224]Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama:
Classification with Rejection Based on Cost-sensitive Classification. ICML 2021: 1507-1517 - [c223]Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama:
Large-Margin Contrastive Learning with Distance Polarization Regularizer. ICML 2021: 1673-1683 - [c222]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c221]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML 2021: 3252-3262 - [c220]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c219]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c218]Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML 2021: 7134-7144 - [c217]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. ICML 2021: 11448-11458 - [c216]Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama:
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. ICML 2021: 11637-11647 - [c215]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. ICML 2021: 11693-11703 - [c214]Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama:
Lower-Bounded Proper Losses for Weakly Supervised Classification. ICML 2021: 12110-12120 - [c213]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML 2021: 12501-12512 - [c212]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS 2021: 6803-6815 - [c211]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c210]Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) 2021: 233-249 - [c209]Takeshi Teshima, Masashi Sugiyama:
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. UAI 2021: 86-96 - [i170]Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
A Symmetric Loss Perspective of Reliable Machine Learning. CoRR abs/2101.01366 (2021) - [i169]Masato Ishii, Masashi Sugiyama:
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics. CoRR abs/2101.10842 (2021) - [i168]Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR abs/2102.00678 (2021) - [i167]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i166]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i165]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR abs/2102.02414 (2021) - [i164]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i163]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. CoRR abs/2102.05311 (2021) - [i162]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. CoRR abs/2102.06879 (2021) - [i161]Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama:
Guided Interpolation for Adversarial Training. CoRR abs/2102.07327 (2021) - [i160]Takeshi Teshima, Masashi Sugiyama:
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. CoRR abs/2103.00136 (2021) - [i159]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. CoRR abs/2103.00719 (2021) - [i158]Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama:
Lower-bounded proper losses for weakly supervised classification. CoRR abs/2103.02893 (2021) - [i157]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Discovering Diverse Solutions in Deep Reinforcement Learning. CoRR abs/2103.07084 (2021) - [i156]Yivan Zhang, Masashi Sugiyama:
Approximating Instance-Dependent Noise via Instance-Confidence Embedding. CoRR abs/2103.13569 (2021) - [i155]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. CoRR abs/2103.17182 (2021) - [i154]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - [i153]Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients. CoRR abs/2105.14900 (2021) - [i152]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i151]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i150]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. CoRR abs/2106.05010 (2021) - [i149]Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. CoRR abs/2106.06152 (2021) - [i148]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i147]Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Multi-Class Classification from Single-Class Data with Confidences. CoRR abs/2106.08864 (2021) - [i146]Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou:
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations. CoRR abs/2106.09256 (2021) - [i145]Shota Nakajima, Masashi Sugiyama:
Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation. CoRR abs/2107.05045 (2021) - [i144]Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama:
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. CoRR abs/2107.08135 (2021) - [i143]Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR abs/2109.14676 (2021) - [i142]Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama:
Rethinking Importance Weighting for Transfer Learning. CoRR abs/2112.10157 (2021) - [i141]Zhenguo Wu, Masashi Sugiyama:
Learning with Proper Partial Labels. CoRR abs/2112.12303 (2021) - 2020
- [j154]Janya Sainui, Masashi Sugiyama:
Unsupervised key frame selection using information theory and colour histogram difference. Int. J. Bus. Intell. Data Min. 16(3): 324-344 (2020) - [j153]Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik:
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. Mach. Learn. 109(3): 513-532 (2020) - [j152]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active deep Q-learning with demonstration. Mach. Learn. 109(9-10): 1699-1725 (2020) - [j151]Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama:
Binary classification with ambiguous training data. Mach. Learn. 109(12): 2369-2388 (2020) - [j150]Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Classification from Triplet Comparison Data. Neural Comput. 32(3): 659-681 (2020) - [j149]Yuangang Pan, Ivor W. Tsang, Avinash Kumar Singh, Chin-Teng Lin, Masashi Sugiyama:
Stochastic Multichannel Ranking with Brain Dynamics Preferences. Neural Comput. 32(8): 1499-1530 (2020) - [j148]Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback. Neural Comput. 32(9): 1733-1773 (2020) - [c208]Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama:
A One-step Approach to Covariate Shift Adaptation. ACML 2020: 65-80 - [c207]Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. AISTATS 2020: 1115-1125 - [c206]Han Bao, Masashi Sugiyama:
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. AISTATS 2020: 2337-2347 - [c205]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. COLT 2020: 408-451 - [c204]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. ICML 2020: 1929-1938 - [c203]Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama:
Learning with Multiple Complementary Labels. ICML 2020: 3072-3081 - [c202]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. ICML 2020: 3337-3347 - [c201]Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML 2020: 4006-4016 - [c200]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020: 4604-4614 - [c199]