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2022 Conference Publication Towards better generalization for neural network-based SAT solversZhang, Chenhao, Zhang, Yanjun, Mao, Jeff, Chen, Weitong, Yue, Lin, Bai, Guangdong and Xu, Miao (2022). Towards better generalization for neural network-based SAT solvers. 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, 16-19 May 2022. CHAM: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-031-05936-0_16 |
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2022 Journal Article Personalized on-device e-health analytics with decentralized block coordinate descentYe, Guanhua, Yin, Hongzhi, Chen, Tong, Xu, Miao, Nguyen, Quoc Viet Hung and Song, Jiangning (2022). Personalized on-device e-health analytics with decentralized block coordinate descent. IEEE Journal of Biomedical and Health Informatics, 26 (6), 1-1. doi: 10.1109/JBHI.2022.3140455 |
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2022 Conference Publication What leads to arrhythmia: active causal representation learning of ECG classificationShen, Shaofei, Chen, Weitong and Xu, Miao (2022). What leads to arrhythmia: active causal representation learning of ECG classification. 35th Australasian Joint Conference, AI 2022, Perth, WA, Australia, 5-8 December 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-22695-3_35 |
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2022 Conference Publication STCT: Spatial-temporal conv-transformer network for cardiac arrhythmias recognitionQiu, Yixuan, Chen, Weitong, Yue, Lin, Xu, Miao and Zhu, Baofeng (2022). STCT: Spatial-temporal conv-transformer network for cardiac arrhythmias recognition. International Conference on Advanced Data Mining and Applications, Sydney, NSW, Australia, 2-4 February 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-95405-5_7 |
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2022 Conference Publication A boosting algorithm for training from only unlabeled dataZhao, Yawen, Yue, Lin and Xu, Miao (2022). A boosting algorithm for training from only unlabeled data. 18th International Conference on Advanced Data Mining and Applications, ADMA 2022, Brisbane, QLD, Australia, 28-30 November 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-22137-8_34 |
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2022 Conference Publication Improving traffic load prediction with multi-modality: a case study of BrisbaneTran, Khai Phan, Chen, Weitong and Xu, Miao (2022). Improving traffic load prediction with multi-modality: a case study of Brisbane. 34th Australasian Joint Conference, AI 2021, Sydney, NSW, Australia, 2-4 February 2022. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-97546-3_21 |
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2022 Conference Publication Investigating active positive-unlabeled learning with deep networksHan, Kun, Chen, Weitong and Xu, Miao (2022). Investigating active positive-unlabeled learning with deep networks. Australasian Joint Conference on Artificial Intelligence (AI), Electr Network, 2-4 February 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-030-97546-3_49 |
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2022 Conference Publication ESTD: Empathy Style Transformer with Discriminative MechanismZhang, Mingzhe, Yue, Lin and Xu, Miao (2022). ESTD: Empathy Style Transformer with Discriminative Mechanism. 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, 28-30 November 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22137-8_5 |
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2021 Conference Publication Multi-hop reading on memory neural network with selective coverage for medication recommendationWang, Yanda, Chen, Weitong, Pi, Dechang, Yue, Lin, Xu, Miao and Li, Xue (2021). Multi-hop reading on memory neural network with selective coverage for medication recommendation. ACM International Conference on Information & Knowledge Management, Virtual Event, 1-5 November 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3459637.3482278 |
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2021 Conference Publication Positive-unlabeled learning from imbalanced dataSu, Guangxin, Chen, Weitong and Xu, Miao (2021). Positive-unlabeled learning from imbalanced data. Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 19-27 August 2021. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2021/412 |
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2021 Conference Publication Self-supervised adversarial distribution regularization for medication recommendationWang, Yanda, Chen, Weitong, PI, Dechang, Yue, Lin, Wang, Sen and Xu, Miao (2021). Self-supervised adversarial distribution regularization for medication recommendation. Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 19-27 August 2021. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2021/431 |
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2021 Conference Publication Pointwise binary classification with pairwise confidence comparisonsFeng, Lei, Shu, Senlin, Lu, Nan, Han, Bo, Xu, Miao, Niu, Gang, An, Bo and Sugiyama, Masashi (2021). Pointwise binary classification with pairwise confidence comparisons. International Conference on Machine Learning (ICML), Virtual, 18-24 July, 2021. San Diego, CA, United States: JMLR. |
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2021 Journal Article Learning from group supervision: the impact of supervision deficiency on multi-label learningXu, Miao and Guo, Lan-Zhe (2021). Learning from group supervision: the impact of supervision deficiency on multi-label learning. Science China Information Sciences, 64 (3) 130101. doi: 10.1007/s11432-020-3132-4 |
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2020 Journal Article Robust multi-label learning with PRO LossXu, Miao, Li, Yu-Feng and Zhou, Zhi-Hua (2020). Robust multi-label learning with PRO Loss. IEEE Transactions on Knowledge and Data Engineering, 32 (8) 8680669, 1610-1624. doi: 10.1109/tkde.2019.2908898 |
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2020 Conference Publication SIGUA: Forgetting may make learning with noisy labels more robustHan, Bo, Niu, Gang, Yu, Xingrui, Yao, Quanming, Xu, Miao, Tsang, Ivor W. and Sugiyama, Masashi (2020). SIGUA: Forgetting may make learning with noisy labels more robust. 37th International Conference on Machine Learning, ICML 2020, Virtual, 13-18 July, 2020. San Diego, CA, United States: JMLR. |
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2020 Conference Publication Trading personalization for accuracy: data debugging in collaborative filteringChen, Long, Yao, Yuan, Xu, Feng, Xu, Miao and Tong, Hanghang (2020). Trading personalization for accuracy: data debugging in collaborative filtering. Conference on Neural Information Processing Systems, Vancouver, Canada, 6-12 December 2020. Maryland Heights, MO, United States: Morgan Kaufmann Publishers. |
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2020 Conference Publication Provably consistent partial-label learningFeng, Lei, Lv, Jiaqi, Han, Bo, Xu, Miao, Niu, Gang, Geng, Xin, An, Bo and Sugiyama, Masashi (2020). Provably consistent partial-label learning. Conference on Neural Information Processing Systems, Vancouver, Canada, 6-12 December 2020. Maryland Heights, MO, United States: Morgan Kaufmann Publishers. |
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2020 Conference Publication Progressive identification of true labels for partial-label learningLvy, Jiaqi, Xu, Miao, Feng, Lei, Niu, Gang, Geng, Xin and Sugiyama, Masashi (2020). Progressive identification of true labels for partial-label learning. 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria, 12-18 July 2020. International Machine Learning Society. |
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2019 Conference Publication Clipped Matrix Completion: A Remedy for Ceiling EffectsTeshima, Takeshi, Xu, Miao, Sato, Issei and Sugiyama, Masashi (2019). Clipped Matrix Completion: A Remedy for Ceiling Effects. Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI United States, 27 January – 1 February 2019. PALO ALTO: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v33i01.33015151 |
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2018 Conference Publication Co-teaching: Robust training of deep neural networks with extremely noisy labelsHan, Bo, Yao, Quanming, Yu, Xingrui, Niu, Gang, Xu, Miao, Hu, Weihua, Tsang, Ivor W. and Sugiyama, Masashi (2018). Co-teaching: Robust training of deep neural networks with extremely noisy labels. 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, Canada, 2-8 December, 2018. Maryland Heights, MO, United States: Morgan Kaufmann Publishers. doi: 10.5555/3327757.3327944 |
