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2020 – today
- 2025
[j194]Wenshui Luo, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong:
Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(1): 305-322 (2025)- 2024
- [j193]Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. RLJ 5: 2140-2161 (2024) - [j192]Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto:
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. J. Medical Syst. 48(1): 84 (2024) - [j191]Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen
, Ning Xie, Gang Niu, Masashi Sugiyama:
Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks 180: 106741 (2024) - [j190]Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 2569-2583 (2024) - [j189]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4398-4409 (2024) - [j188]Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama:
On the Effectiveness of Adversarial Training Against Backdoor Attacks. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14878-14888 (2024) - [c296]Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama:
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. AAAI 2024: 13383-13390 - [c295]Shintaro Nakamura, Masashi Sugiyama:
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. AAAI 2024: 14414-14421 - [c294]Guillaume Braun, Masashi Sugiyama:
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. AISTATS 2024: 532-540 - [c293]Shintaro Nakamura, Masashi Sugiyama:
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. AISTATS 2024: 1225-1233 - [c292]Masashi Sugiyama:
Overcoming Continuous Distribution Shifts: Challenges in Online Machine Learning. BCI 2024: 1-3 - [c291]Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama:
Direct Distillation Between Different Domains. ECCV (80) 2024: 154-172 - [c290]Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. ECCV (52) 2024: 437-454 - [c289]Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama:
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. EMNLP 2024: 14394-14410 - [c288]Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama:
Robust Similarity Learning with Difference Alignment Regularization. ICLR 2024 - [c287]Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. ICLR 2024 - [c286]Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama:
Accurate Forgetting for Heterogeneous Federated Continual Learning. ICLR 2024 - [c285]Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
A General Framework for Learning from Weak Supervision. ICML 2024 - [c284]Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. ICML 2024 - [c283]Yuyang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou:
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. ICML 2024 - [c282]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. ICML 2024 - [c281]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML 2024 - [c280]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. ICML 2024 - [c279]Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. ICML 2024 - [c278]Qingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama:
An offline learning of behavior correction policy for vision-based robotic manipulation. ICRA 2024: 5448-5454 - [c277]Yuki Tanaka, Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama:
Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data. WACV 2024: 2768-2777 - [i238]Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama:
Direct Distillation between Different Domains. CoRR abs/2401.06826 (2024) - [i237]Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
A General Framework for Learning from Weak Supervision. CoRR abs/2402.01922 (2024) - [i236]Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama:
Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution. CoRR abs/2402.03771 (2024) - [i235]Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. CoRR abs/2402.06918 (2024) - [i234]Guillaume Braun, Masashi Sugiyama:
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. CoRR abs/2402.18805 (2024) - [i233]Hao Chen, Jindong Wang, Zihan Wang, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Learning with Noisy Foundation Models. CoRR abs/2403.06869 (2024) - [i232]Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric:
Reinforcement Learning with Options and State Representation. CoRR abs/2403.10855 (2024) - [i231]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR abs/2404.06287 (2024) - [i230]Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama:
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. CoRR abs/2404.07465 (2024) - [i229]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. CoRR abs/2405.09892 (2024) - [i228]Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. CoRR abs/2405.14114 (2024) - [i227]Or Raveh, Junya Honda, Masashi Sugiyama:
Multi-Player Approaches for Dueling Bandits. CoRR abs/2405.16168 (2024) - [i226]Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. CoRR abs/2405.18890 (2024) - [i225]Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj:
Slight Corruption in Pre-training Data Makes Better Diffusion Models. CoRR abs/2405.20494 (2024) - [i224]Jianing Zhu, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama:
Decoupling the Class Label and the Target Concept in Machine Unlearning. CoRR abs/2406.08288 (2024) - [i223]Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama:
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR abs/2406.09179 (2024) - [i222]Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR abs/2407.18624 (2024) - [i221]Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama:
Adaptive complexity of log-concave sampling. CoRR abs/2408.13045 (2024) - [i220]Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama:
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. CoRR abs/2409.16718 (2024) - [i219]Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
On Unsupervised Prompt Learning for Classification with Black-box Language Models. CoRR abs/2410.03124 (2024) - [i218]Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang:
Sharpness-Aware Black-Box Optimization. CoRR abs/2410.12457 (2024) - [i217]Yuting Tang, Xin-Qiang Cai, Jing-Cheng Pang, Qiyu Wu, Yao-Xiang Ding, Masashi Sugiyama:
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning. CoRR abs/2410.20176 (2024) - 2023
- [j187]Yosuke Otsubo, Naoya Otani, Megumi Chikasue, Mineyuki Nishino, Masashi Sugiyama:
Root cause estimation of faults in production processes: a novel approach inspired by approximate Bayesian computation. Int. J. Prod. Res. 61(5): 1556-1574 (2023) - [j186]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal Approximation Property of Invertible Neural Networks. J. Mach. Learn. Res. 24: 287:1-287:68 (2023) - [j185]Shota Nakajima, Masashi Sugiyama:
Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation. Mach. Learn. 112(3): 889-919 (2023) - [j184]Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Boundary-restricted metric learning. Mach. Learn. 112(12): 4723-4762 (2023) - [j183]Zhenguo Wu, Jiaqi Lv, Masashi Sugiyama:
Learning With Proper Partial Labels. Neural Comput. 35(1): 58-81 (2023) - [j182]Tingting Zhao, S. Wu, G. Li, Y. Chen, Gang Niu, Masashi Sugiyama:
Learning Intention-Aware Policies in Deep Reinforcement Learning. Neural Comput. 35(10): 1657-1677 (2023) - [j181]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation learning for continuous action spaces is beneficial for efficient policy learning. Neural Networks 159: 137-152 (2023) - [j180]Chen Gong, Yongliang Ding, Bo Han, Gang Niu, Jian Yang, Jane You, Dacheng Tao, Masashi Sugiyama:
Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2835-2848 (2023) - [c276]Jongyeong Lee, Junya Honda, Masashi Sugiyama:
Thompson Exploration with Best Challenger Rule in Best Arm Identification. ACML 2023: 646-661 - [c275]Nobutaka Ito, Masashi Sugiyama:
Audio Signal Enhancement with Learning from Positive and Unlabeled Data. ICASSP 2023: 1-5 - [c274]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. ICCV 2023: 17225-17234 - [c273]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. ICCV 2023: 17424-17434 - [c272]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. ICLR 2023 - [c271]Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou:
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. ICLR 2023 - [c270]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c269]Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. ICML 2023: 11255-11282 - [c268]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML 2023: 18810-18851 - [c267]Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. ICML 2023: 41596-41612 - [c266]Xin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama, Ashley Llorens:
Distributional Pareto-Optimal Multi-Objective Reinforcement Learning. NeurIPS 2023 - [c265]Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama:
Imitation Learning from Vague Feedback. NeurIPS 2023 - [c264]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. NeurIPS 2023 - [c263]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. NeurIPS 2023 - [c262]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS 2023 - [c261]Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama:
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. NeurIPS 2023 - [c260]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. NeurIPS 2023 - [c259]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. NeurIPS 2023 - [c258]Yu-Jie Zhang, Masashi Sugiyama:
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost. NeurIPS 2023 - [c257]Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama:
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. NeurIPS 2023 - [c256]Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS 2023 - [i216]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. CoRR abs/2302.01544 (2023) - [i215]Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama:
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. CoRR abs/2302.02552 (2023) - [i214]Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. CoRR abs/2302.02907 (2023) - [i213]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. CoRR abs/2302.03857 (2023) - [i212]Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama:
Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. CoRR abs/2302.14407 (2023) - [i211]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i210]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. CoRR abs/2305.00374 (2023) - [i209]Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama:
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR abs/2305.00399 (2023) - [i208]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR abs/2305.02795 (2023) - [i207]Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. CoRR abs/2305.06886 (2023) - [i206]Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR abs/2305.08344 (2023) - [i205]Sora Satake, Yoshihiro Nagano, Masashi Sugiyama, Masahiro Fujiwara, Yasutoshi Makino, Hiroyuki Shinoda:
Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model. CoRR abs/2305.09412 (2023) - [i204]Yivan Zhang, Masashi Sugiyama:
Enriching Disentanglement: Definitions to Metrics. CoRR abs/2305.11512 (2023) - [i203]Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. CoRR abs/2305.12715 (2023) - [i202]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR abs/2305.14690 (2023) - [i201]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR abs/2305.18377 (2023) - [i200]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i199]Shintaro Nakamura, Masashi Sugiyama:
Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class. CoRR abs/2306.09202 (2023) - [i198]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR abs/2307.05948 (2023) - [i197]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR abs/2307.11469 (2023) - [i196]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. CoRR abs/2308.06453 (2023) - [i195]Shintaro Nakamura, Masashi Sugiyama:
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR abs/2308.10238 (2023) - [i194]Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. CoRR abs/2309.08216 (2023) - [i193]Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. CoRR abs/2309.17002 (2023) - [i192]Jongyeong Lee, Junya Honda, Masashi Sugiyama:
Thompson Exploration with Best Challenger Rule in Best Arm Identification. CoRR abs/2310.00539 (2023) - [i191]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. CoRR abs/2310.05632 (2023) - [i190]Wentao Yu, Shuo Chen, Chen Gong, Gang Niu, Masashi Sugiyama:
Atom-Motif Contrastive Transformer for Molecular Property Prediction. CoRR abs/2310.07351 (2023) - [i189]Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR abs/2310.13923 (2023) - [i188]Shintaro Nakamura, Masashi Sugiyama:
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR abs/2310.15681 (2023) - [i187]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR abs/2311.15502 (2023) - 2022
- [j179]Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima:
Improving imbalanced classification using near-miss instances. Expert Syst. Appl. 201: 117130 (2022) - [j178]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) - [j177]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) - [j176]Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama:
Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. 23: 307:1-307:34 (2022) - [j175]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) - [j174]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) - [j173]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) - [j172]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) - [j171]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) - [j170]