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2024 (5)

Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi, Mohammad Hossein Rohban, & Mohammad Sabokrou. Universal Novelty Detection through Adaptive Contrastive Learning. In CVPR, 2024.
link bibtex

@inproceedings{mohammadcvpr2024-1,
  author    = {Hossein Mirzaei and Mojtaba Nafez and Mohammad Jafari and Mohammad Bagher Soltani and Mohammad Azizmalayeri and Jafar Habibi and Mohammad Hossein Rohban and Mohammad Sabokrou},
  title     = {Universal Novelty Detection through Adaptive Contrastive Learning},
  booktitle = {CVPR},
  year      = {2024}
}

Mozhgan Pourkeshavarz, Mohammad Sabokrou, & Amir Rasouli. Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving. In CVPR, 2024.
link bibtex

@inproceedings{mohammadcvpr2024-2,
  author    = {Mozhgan Pourkeshavarz and Mohammad Sabokrou and Amir Rasouli},
  title     = {Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving},
  booktitle = {CVPR},
  year      = {2024}
}

Guillaume Houry, Han Bao, Han Zhao, & Makoto Yamada. Fast 1-Wasserstein distance approximations using greedy strategies. In AISTATS, 2024.
link bibtex

@inproceedings{aistats_guillaume,
  author    = {Guillaume Houry and Han Bao and Han Zhao and Makoto Yamada},
  title     = {Fast 1-Wasserstein distance approximations using greedy strategies},
  booktitle = {AISTATS},
  year      = {2024}
}

Haoyu Han, Xiaorui Liu, Li Ma, MohamadAli Torkamani, Hui Liu, Jiliang Tang, & Makoto Yamada. Structural Fairness-aware Active Learning for Graph Neural Networks. In ICLR, 2024.
link bibtex

@inproceedings{iclr2024,
  author    = {Haoyu Han and Xiaorui Liu and Li Ma and MohamadAli Torkamani and Hui Liu and Jiliang Tang and Makoto Yamada},
  title     = {Structural Fairness-aware Active Learning for Graph Neural Networks},
  booktitle = {ICLR},
  year      = {2024}
}

Peter Naylor, Diego Di Carlo, Arianna Traviglia, Makoto Yamada, & Marco Fiorucci. Implicit neural representation for change detection. In WACV, 2024.
link bibtex

@inproceedings{naylor2024implicit,
title={Implicit neural representation for change detection},
  author={Naylor, Peter and Di Carlo, Diego and Traviglia, Arianna and Yamada, Makoto and Fiorucci, Marco},
  booktitle = {WACV},
  year      = {2024}
}

2023 (14)

Sho Otao, & Makoto Yamada. A linear time approximation of Wasserstein distance with word embedding selection. In EMNLP, 2023.
link bibtex

@inproceedings{otaoEMNLP2023,
  author    = {Sho Otao and Makoto Yamada},
  title     = {A linear time approximation of Wasserstein distance with word embedding selection},
  booktitle = {EMNLP},
  year      = {2023}
}

Cléa Laouar, Yuki Takezawa, & Makoto Yamada. Large-scale similarity search with Optimal Transport. In EMNLP, 2023.
link bibtex

@inproceedings{laouarEMNLP2023,
  author    = {Cléa Laouar and Yuki Takezawa and Makoto Yamada},
  title     = {Large-scale similarity search with Optimal Transport},
  booktitle = {EMNLP},
  year      = {2023}
}

Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, & Makoto Yamada. Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. In NeurIPS, 2023.
link bibtex

@inproceedings{takezawa2023neurips,
author={Yuki Takezawa and Ryoma Sato and Han Bao and Kenta Niwa and Makoto Yamada},
  title     = {Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence},
  booktitle = {NeurIPS},
  year      = {2023}
}

Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, & Makoto Yamada. Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. Transactions on Machine Learning Research. 2023.

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data [link]

Paper link bibtex 11 downloads

@article{
takezawa2023momentum,
title={Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data},
author={Yuki Takezawa and Han Bao and Kenta Niwa and Ryoma Sato and Makoto Yamada},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=8koy8QuTZD},
note={}
}

Raha Ahmadi, Mohammad Javad Rajabi, Mohammad Khalooiem, & Mohamamd Sabokrou. Mitigating Bias: Enhancing Image Classification by Improving Model Explanations. In ACML, 2023.
link bibtex

@inproceedings{ahmadi2023mitigating,
author={Ahmadi, Raha and Rajabi, Mohammad Javad and  Mohammad Khalooiem  and Mohamamd Sabokrou},
  title     = {Mitigating Bias: Enhancing Image Classification by Improving Model Explanations},
  booktitle = {ACML},
  year      = {2023}
}

Yuki Takezawa, Kenta Niwa, & Makoto Yamada. Communication compression for decentralized learning with operator splitting methods. IEEE Transactions on Signal and Information Processing over Networks. 2023.
link bibtex

@article{takezawa2022communication,
  title={Communication compression for decentralized learning with operator splitting methods},
  author={Takezawa, Yuki and Niwa, Kenta and Yamada, Makoto},
  journal={ IEEE Transactions on Signal and Information Processing over Networks},
  year={2023}
}

Yanbin Liu, Girish Dwivedi, Farid Boussaid, Frank Sanfilippo, Makoto Yamada, & Mohammed Bennamoun. Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images. Computer Methods and Programs in Biomedicine,107685. 2023.

Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images [link]

Paper doi link bibtex 14 downloads

@article{LIU2023107685,
title = {Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images},
journal = {Computer Methods and Programs in Biomedicine},
pages = {107685},
year = {2023},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2023.107685},
url = {https://www.sciencedirect.com/science/article/pii/S0169260723003504},
author = {Yanbin Liu and Girish Dwivedi and Farid Boussaid and Frank Sanfilippo and Makoto Yamada and Mohammed Bennamoun},
}

Marco Fiorucci, Peter Naylor, & Makoto Yamada. Optimal Transport for Change Detection on LiDAR Point Clouds. IGARSS. 2023.
link bibtex

@article{fiorucci2023optimal,
  title={Optimal Transport for Change Detection on LiDAR Point Clouds},
  author={Fiorucci, Marco and Naylor, Peter and Yamada, Makoto},
  journal={IGARSS},
  year={2023}
}

Dinesh Singh, Héctor Climente-González, Mathis Petrovich, Eiryo Kawakami, & Makoto Yamada. Fsnet: Feature selection network on high-dimensional biological data. IJCNN. 2023.
link bibtex

@article{singh2023fsnet,
  title={Fsnet: Feature selection network on high-dimensional biological data},
  author={Singh, Dinesh and Climente-Gonz{\'a}lez, H{\'e}ctor and Petrovich, Mathis and Kawakami, Eiryo and Yamada, Makoto},
  journal={IJCNN},
  year={2023}
}

Yanbin Liu, Linchao Zhu, Xiaohan Wang, Makoto Yamada, & Yi Yang. Bilaterally-normalized Scale-consistent Sinkhorn Distance for Few-shot Image Classification. IEEE Transactions on Neural Networks and Learning Systems. 2023.
doi link bibtex 4 downloads

@ARTICLE{TNNLS2023_BSSD,
                    author={Liu, Yanbin and Zhu, Linchao and Wang, Xiaohan and Yamada, Makoto and Yang, Yi},
                    journal={IEEE Transactions on Neural Networks and Learning Systems}, 
                    title={Bilaterally-normalized Scale-consistent Sinkhorn Distance for Few-shot Image Classification}, 
                    year={2023},
                    doi={10.1109/TNNLS.2023.3262351}}

Ryuichiro Hataya, & Makoto Yamada. Nyström Method for Accurate and Scalable Implicit Differentiation. In AISTATS, 2023.
link bibtex

@inproceedings{aistats_hataya,
  author    = {Ryuichiro Hataya and
               Makoto Yamada},
  title     = {Nyström Method for Accurate and Scalable Implicit Differentiation},
  booktitle = {AISTATS},
  year      = {2023}
}

Weijie Liu, Jiahao Xie, Chao Zhang, Makoto Yamada, Nenggan Zheng, & Hui Qian. Robust Graph Dictionary Learning. In ICLR, 2023.
link bibtex

@inproceedings{iclr2,
  author    = {Weijie Liu and Jiahao Xie and Chao Zhang and Makoto Yamada and Nenggan Zheng and Hui Qian},
  title     = {Robust Graph Dictionary Learning},
  booktitle = {ICLR},
  year      = {2023}
}

Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves, Cees G. M. Snoek, Mohammad Sabokrou, & Mohammad Hossein Rohban. Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models. In ICLR, 2023.
link bibtex

@inproceedings{DBLP:journals/corr/abs-2205-14297,
  author    = {Hossein Mirzaei and
               Mohammadreza Salehi and
               Sajjad Shahabi and
               Efstratios Gavves and
               Cees G. M. Snoek and
               Mohammad Sabokrou and
               Mohammad Hossein Rohban},
  title     = {Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based
               Generative Models},
  booktitle = {ICLR},
  year      = {2023}
}

Héctor Climente-González, Chloé-Agathe Azencott, & Makoto Yamada. A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selection. STAR protocols, 4(1): 101998. 2023.
link bibtex

@article{climente2023network,
  title={A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selection},
  author={Climente-Gonz{\'a}lez, H{\'e}ctor and Azencott, Chlo{\'e}-Agathe and Yamada, Makoto},
  journal={STAR protocols},
  volume={4},
  number={1},
  pages={101998},
  year={2023},
  publisher={Elsevier}
}

2022 (9)

Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, & Yi Chang. Graphlime: Local interpretable model explanations for graph neural networks. IEEE Transactions on Knowledge and Data Engineering. 2022.
link bibtex

@article{huang2022graphlime,
  title={Graphlime: Local interpretable model explanations for graph neural networks},
  author={Huang, Qiang and Yamada, Makoto and Tian, Yuan and Singh, Dinesh and Chang, Yi},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022},
  publisher={IEEE}
}

Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, & Sujith Ravi. Approximating 1-Wasserstein Distance with Trees. Transactions on Machine Learning Research. 2022.

Approximating 1-Wasserstein Distance with Trees [link]

Paper link bibtex 5 downloads

@article{yamada2022approximating,
  title={Approximating 1-Wasserstein Distance with Trees},
  author={Yamada, Makoto and Takezawa, Yuki and Sato, Ryoma and Bao, Han and Kozareva, Zornitsa and Ravi, Sujith},
  journal={Transactions on Machine Learning Research},
  url       = {https://doi.org/10.1016/j.joi.2022.101283},
  year={2022}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Poincare: Recommending Publication Venues via Treatment Effect Estimation. J. Informetrics, 16(2): 101283. 2022.

Poincare: Recommending Publication Venues via Treatment Effect Estimation [link]

Paper doi link bibtex 1 download

@article{DBLP:journals/joi/SatoYK22,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Poincare: Recommending Publication Venues via Treatment Effect Estimation},
  journal   = {J. Informetrics},
  volume    = {16},
  number    = {2},
  pages     = {101283},
  year      = {2022},
  url       = {https://doi.org/10.1016/j.joi.2022.101283},
  doi       = {10.1016/j.joi.2022.101283},
  timestamp = {Mon, 25 Jul 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/joi/SatoYK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Constant Time Graph Neural Networks. ACM Trans. Knowl. Discov. Data, 16(5): 92:1–92:31. 2022.

Constant Time Graph Neural Networks [link]

Paper doi link bibtex 6 downloads

@article{DBLP:journals/tkdd/SatoYK22,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Constant Time Graph Neural Networks},
  journal   = {{ACM} Trans. Knowl. Discov. Data},
  volume    = {16},
  number    = {5},
  pages     = {92:1--92:31},
  year      = {2022},
  url       = {https://doi.org/10.1145/3502733},
  doi       = {10.1145/3502733},
  timestamp = {Mon, 13 Jun 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/tkdd/SatoYK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, & Makoto Yamada. Fixed Support Tree-Sliced Wasserstein Barycenter. In AISTATS, 2022.

Fixed Support Tree-Sliced Wasserstein Barycenter [link]

Paper link bibtex 1 download

@inproceedings{DBLP:conf/aistats/TakezawaSKRY22,
  author    = {Yuki Takezawa and
               Ryoma Sato and
               Zornitsa Kozareva and
               Sujith Ravi and
               Makoto Yamada},
  title     = {Fixed Support Tree-Sliced Wasserstein Barycenter},
  booktitle = {AISTATS},
  year      = {2022},
  url       = {https://proceedings.mlr.press/v151/takezawa22a.html},
  timestamp = {Fri, 20 May 2022 10:03:18 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/TakezawaSKRY22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Benjamin Poignard, Peter J. Naylor, Héctor Climente-González, & Makoto Yamada. Feature screening with kernel knockoffs. In Gustau Camps-Valls, Francisco J. R. Ruiz, & Isabel Valera., editor(s), AISTATS, 2022.

Feature screening with kernel knockoffs [link]

Paper link bibtex 1 download

@inproceedings{DBLP:conf/aistats/PoignardNCY22,
  author    = {Benjamin Poignard and
               Peter J. Naylor and
               H{\'{e}}ctor Climente{-}Gonz{\'{a}}lez and
               Makoto Yamada},
  editor    = {Gustau Camps{-}Valls and
               Francisco J. R. Ruiz and
               Isabel Valera},
  title     = {Feature screening with kernel knockoffs},
  booktitle = {AISTATS},
  year      = {2022},
  url       = {https://proceedings.mlr.press/v151/poignard22a.html},
  timestamp = {Fri, 20 May 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/PoignardNCY22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling. In Mohammad Al Hasan, & Li Xiong., editor(s), CIKM, 2022.

Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling [link]

Paper doi link bibtex 1 download

@inproceedings{DBLP:conf/cikm/SatoYK22,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  editor    = {Mohammad Al Hasan and
               Li Xiong},
  title     = {Twin Papers: {A} Simple Framework of Causal Inference for Citations
               via Coupling},
  booktitle = {CIKM},
  year      = {2022},
  url       = {https://doi.org/10.1145/3511808.3557716},
  doi       = {10.1145/3511808.3557716},
  timestamp = {Wed, 19 Oct 2022 12:52:00 +0200},
  biburl    = {https://dblp.org/rec/conf/cikm/SatoYK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Re-evaluating Word Mover's Distance. In ICML, 2022.

Re-evaluating Word Mover's Distance [link]

Paper link bibtex

@inproceedings{DBLP:conf/icml/SatoYK22,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Re-evaluating Word Mover's Distance},
  booktitle = {ICML},
  year      = {2022},
  url       = {https://proceedings.mlr.press/v162/sato22b.html},
  timestamp = {Tue, 12 Jul 2022 17:36:52 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/SatoYK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yoichi Chikahara, Makoto Yamada, & Hisashi Kashima. Feature selection for discovering distributional treatment effect modifiers. In James Cussens, & Kun Zhang., editor(s), UAI, 2022.

Feature selection for discovering distributional treatment effect modifiers [link]

Paper link bibtex 1 download

@inproceedings{DBLP:conf/uai/ChikaharaYK22,
  author    = {Yoichi Chikahara and
               Makoto Yamada and
               Hisashi Kashima},
  editor    = {James Cussens and
               Kun Zhang},
  title     = {Feature selection for discovering distributional treatment effect
               modifiers},
  booktitle = {UAI},
  year      = {2022},
  url       = {https://proceedings.mlr.press/v180/chikahara22a.html},
  timestamp = {Sat, 15 Oct 2022 12:08:13 +0200},
  biburl    = {https://dblp.org/rec/conf/uai/ChikaharaYK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

2021 (10)

Mari Hashimoto, Yoriko Saito, Ryo Nakagawa, Ikuko Ogahara, Shinsuke Takagi, Sadaaki Takata, Hanae Amitani, Mikiko Endo, Hitomi Yuki, Jordan A Ramilowski, & others. Combined inhibition of XIAP and BCL2 drives maximal therapeutic efficacy in genetically diverse aggressive acute myeloid leukemia. Nature Cancer, 2(3): 340–356. 2021.
link bibtex

@article{hashimoto2021combined,
  title={Combined inhibition of XIAP and BCL2 drives maximal therapeutic efficacy in genetically diverse aggressive acute myeloid leukemia},
  author={Hashimoto, Mari and Saito, Yoriko and Nakagawa, Ryo and Ogahara, Ikuko and Takagi, Shinsuke and Takata, Sadaaki and Amitani, Hanae and Endo, Mikiko and Yuki, Hitomi and Ramilowski, Jordan A and others},
  journal={Nature Cancer},
  volume={2},
  number={3},
  pages={340--356},
  year={2021},
  publisher={Nature Publishing Group US New York}
}

Tam Le, Nhat Ho, & Makoto Yamada. Flow-based Alignment Approaches for Probability Measures in Different Spaces. In AISTATS, 2021.

Flow-based Alignment Approaches for Probability Measures in Different Spaces [link]

Paper link bibtex 11 downloads

@inproceedings{DBLP:conf/aistats/LeHY21,
  author    = {Tam Le and
               Nhat Ho and
               Makoto Yamada},
  title     = {Flow-based Alignment Approaches for Probability Measures in Different
               Spaces},
  booktitle = {AISTATS},
  year      = {2021},
  url       = {http://proceedings.mlr.press/v130/le21b.html},
  timestamp = {Wed, 14 Apr 2021 16:51:37 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/LeHY21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ayato Toyokuni, Sho Yokoi, Hisashi Kashima, & Makoto Yamada. Computationally Efficient Wasserstein Loss for Structured Labels. In EACL, 2021.

Computationally Efficient Wasserstein Loss for Structured Labels [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/eacl/ToyokuniYKY21,
  author    = {Ayato Toyokuni and
               Sho Yokoi and
               Hisashi Kashima and
               Makoto Yamada},
  title     = {Computationally Efficient Wasserstein Loss for Structured Labels},
  booktitle = {EACL},
  year      = {2021},
  url       = {https://doi.org/10.18653/v1/2021.eacl-srw.1},
  doi       = {10.18653/v1/2021.eacl-srw.1},
  timestamp = {Thu, 20 Jan 2022 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/eacl/ToyokuniYKY21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Tobias Freidling, Benjamin Poignard, Héctor Climente-González, & Makoto Yamada. Post-selection inference with HSIC-Lasso. In ICML, 2021.

Post-selection inference with HSIC-Lasso [link]

Paper link bibtex 1 download

@inproceedings{DBLP:conf/icml/FreidlingPCY21,
  author    = {Tobias Freidling and
               Benjamin Poignard and
               H{\'{e}}ctor Climente{-}Gonz{\'{a}}lez and
               Makoto Yamada},
  title     = {Post-selection inference with HSIC-Lasso},
  booktitle = {ICML},
  year      = {2021},
  url       = {http://proceedings.mlr.press/v139/freidling21a.html},
  timestamp = {Wed, 25 Aug 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/FreidlingPCY21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Vu Nguyen, Tam Le, Makoto Yamada, & Michael A. Osborne. Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. In ICML, 2021.

Paper link bibtex

@inproceedings{DBLP:conf/icml/NguyenLYO21,
  author    = {Vu Nguyen and
               Tam Le and
               Makoto Yamada and
               Michael A. Osborne},
  title     = {Optimal Transport Kernels for Sequential and Parallel Neural Architecture
               Search},
  booktitle = {ICML},
  year      = {2021},
  url       = {http://proceedings.mlr.press/v139/nguyen21d.html},
  timestamp = {Wed, 25 Aug 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/NguyenLYO21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yuki Takezawa, Ryoma Sato, & Makoto Yamada. Supervised Tree-Wasserstein Distance. In ICML, 2021.

Supervised Tree-Wasserstein Distance [link]

Paper link bibtex 4 downloads

@inproceedings{DBLP:conf/icml/TakezawaSY21,
  author    = {Yuki Takezawa and
               Ryoma Sato and
               Makoto Yamada},
  title     = {Supervised Tree-Wasserstein Distance},
  booktitle = {ICML},
  year      = {2021},
  url       = {http://proceedings.mlr.press/v139/takezawa21a.html},
  timestamp = {Wed, 25 Aug 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/TakezawaSY21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Hiroaki Yamada, & Makoto Yamada. Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares. In NeurIPS, 2021.

Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/YamadaY21,
  author    = {Hiroaki Yamada and
               Makoto Yamada},
  title     = {Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares},
  booktitle = {NeurIPS},
  year      = {2021},
  url       = {https://proceedings.neurips.cc/paper/2021/hash/7b5b23f4aadf9513306bcd59afb6e4c9-Abstract.html},
  timestamp = {Tue, 03 May 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/YamadaY21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Tam Le, Truyen Nguyen, Makoto Yamada, Jose H. Blanchet, & Viet Anh Nguyen. Adversarial Regression with Doubly Non-negative Weighting Matrices. In NeurIPS, 2021.

Adversarial Regression with Doubly Non-negative Weighting Matrices [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/LeNYBN21,
  author    = {Tam Le and
               Truyen Nguyen and
               Makoto Yamada and
               Jose H. Blanchet and
               Viet Anh Nguyen},
  title     = {Adversarial Regression with Doubly Non-negative Weighting Matrices},
  booktitle = {NeurIPS},
  year      = {2021},
  url       = {https://proceedings.neurips.cc/paper/2021/hash/8cfef17bee2b7a75a3ce09d40b497f6b-Abstract.html},
  timestamp = {Tue, 03 May 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/LeNYBN21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, & Yi Yang. LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport. In ECML, 2021.

LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/pkdd/LiuYTLSY21,
  author    = {Yanbin Liu and
               Makoto Yamada and
               Yao{-}Hung Hubert Tsai and
               Tam Le and
               Ruslan Salakhutdinov and
               Yi Yang},
  title     = {LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with
               Optimal Transport},
  booktitle = {ECML},
  year      = {2021},
  url       = {https://doi.org/10.1007/978-3-030-86486-6\_40},
  doi       = {10.1007/978-3-030-86486-6\_40},
  timestamp = {Mon, 24 Jan 2022 07:47:58 +0100},
  biburl    = {https://dblp.org/rec/conf/pkdd/LiuYTLSY21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Random Features Strengthen Graph Neural Networks. In SDM, 2021.

Random Features Strengthen Graph Neural Networks [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/sdm/SatoYK21,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Random Features Strengthen Graph Neural Networks},
  booktitle = {SDM},
  year      = {2021},
  url       = {https://doi.org/10.1137/1.9781611976700.38},
  doi       = {10.1137/1.9781611976700.38},
  timestamp = {Sat, 21 Aug 2021 11:46:54 +0200},
  biburl    = {https://dblp.org/rec/conf/sdm/SatoYK21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

2020 (9)

Kishan Wimalawarne, Makoto Yamada, & Hiroshi Mamitsuka. Scaled Coupled Norms and Coupled Higher-Order Tensor Completion. Neural Comput., 32(2): 447–484. 2020.

Scaled Coupled Norms and Coupled Higher-Order Tensor Completion [link]

Paper doi link bibtex

@article{DBLP:journals/neco/WimalawarneYM20,
  author    = {Kishan Wimalawarne and
               Makoto Yamada and
               Hiroshi Mamitsuka},
  title     = {Scaled Coupled Norms and Coupled Higher-Order Tensor Completion},
  journal   = {Neural Comput.},
  volume    = {32},
  number    = {2},
  pages     = {447--484},
  year      = {2020},
  url       = {https://doi.org/10.1162/neco\_a\_01254},
  doi       = {10.1162/neco\_a\_01254},
  timestamp = {Sat, 05 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/neco/WimalawarneYM20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Qiang Huang, Tingyu Xia, Huiyan Sun, Makoto Yamada, & Yi Chang. Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks. In AAAI, 2020.

Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks [link]

Paper link bibtex

@inproceedings{DBLP:conf/aaai/HuangXSYC20,
  author    = {Qiang Huang and
               Tingyu Xia and
               Huiyan Sun and
               Makoto Yamada and
               Yi Chang},
  title     = {Unsupervised Nonlinear Feature Selection from High-Dimensional Signed
               Networks},
  booktitle = {AAAI},
  year      = {2020},
  url       = {https://ojs.aaai.org/index.php/AAAI/article/view/5839},
  timestamp = {Mon, 07 Mar 2022 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/aaai/HuangXSYC20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Benjamin Poignard, & Makoto Yamada. Sparse Hilbert-Schmidt Independence Criterion Regression. In AISTATS, 2020.

Sparse Hilbert-Schmidt Independence Criterion Regression [link]

Paper link bibtex

@inproceedings{DBLP:conf/aistats/PoignardY20,
  author    = {Benjamin Poignard and
               Makoto Yamada},
  title     = {Sparse Hilbert-Schmidt Independence Criterion Regression},
  booktitle = {AISTATS},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/poignard20a.html},
  timestamp = {Mon, 29 Jun 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/PoignardY20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, & Hidetoshi Shimodaira. More Powerful Selective Kernel Tests for Feature Selection. In AISTATS, 2020.

More Powerful Selective Kernel Tests for Feature Selection [link]

Paper link bibtex 1 download

@inproceedings{DBLP:conf/aistats/LimYJTMS20,
  author    = {Jen Ning Lim and
               Makoto Yamada and
               Wittawat Jitkrittum and
               Yoshikazu Terada and
               Shigeyuki Matsui and
               Hidetoshi Shimodaira},
  title     = {More Powerful Selective Kernel Tests for Feature Selection},
  booktitle = {AISTATS},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/lim20a.html},
  timestamp = {Mon, 29 Jun 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/LimYJTMS20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yanbin Liu, Linchao Zhu, Makoto Yamada, & Yi Yang. Semantic Correspondence as an Optimal Transport Problem. In CVPR, 2020.

Semantic Correspondence as an Optimal Transport Problem [link]

Paper doi link bibtex 1 download

@inproceedings{DBLP:conf/cvpr/LiuZYY20,
  author    = {Yanbin Liu and
               Linchao Zhu and
               Makoto Yamada and
               Yi Yang},
  title     = {Semantic Correspondence as an Optimal Transport Problem},
  booktitle = {CVPR},
  year      = {2020},
  url       = {https://openaccess.thecvf.com/content\_CVPR\_2020/html/Liu\_Semantic\_Correspondence\_as\_an\_Optimal\_Transport\_Problem\_CVPR\_2020\_paper.html},
  doi       = {10.1109/CVPR42600.2020.00452},
  timestamp = {Wed, 27 Oct 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/cvpr/LiuZYY20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Luu Huu Phuc, Koh Takeuchi, Makoto Yamada, & Hisashi Kashima. Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport. In DSAA, 2020.

Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/dsaa/PhucTYK20,
  author    = {Luu Huu Phuc and
               Koh Takeuchi and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding
               and Optimal Transport},
  booktitle = {DSAA},
  year      = {2020},
  url       = {https://doi.org/10.1109/DSAA49011.2020.00037},
  doi       = {10.1109/DSAA49011.2020.00037},
  timestamp = {Tue, 24 Nov 2020 12:27:16 +0100},
  biburl    = {https://dblp.org/rec/conf/dsaa/PhucTYK20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Tatsuya Shiraishi, Tam Le, Hisashi Kashima, & Makoto Yamada. Topological Bayesian Optimization with Persistence Diagrams. In ECAI, 2020.

Topological Bayesian Optimization with Persistence Diagrams [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/ecai/ShiraishiLKY20,
  author    = {Tatsuya Shiraishi and
               Tam Le and
               Hisashi Kashima and
               Makoto Yamada},
  title     = {Topological Bayesian Optimization with Persistence Diagrams},
  booktitle = {ECAI},
  year      = {2020},
  url       = {https://doi.org/10.3233/FAIA200255},
  doi       = {10.3233/FAIA200255},
  timestamp = {Fri, 09 Apr 2021 18:50:05 +0200},
  biburl    = {https://dblp.org/rec/conf/ecai/ShiraishiLKY20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Fast Unbalanced Optimal Transport on a Tree. In NeurIPS, 2020.

Fast Unbalanced Optimal Transport on a Tree [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/SatoYK20,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Fast Unbalanced Optimal Transport on a Tree},
  booktitle = {NeurIPS},
  year      = {2020},
  url       = {https://proceedings.neurips.cc/paper/2020/hash/dba31bb5c75992690f20c2d3b370ec7c-Abstract.html},
  timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/SatoYK20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, & Ruslan Salakhutdinov. Neural Methods for Point-wise Dependency Estimation. In NeurIPS, 2020.

Neural Methods for Point-wise Dependency Estimation [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/Tsai0YMS20,
  author    = {Yao{-}Hung Hubert Tsai and
               Han Zhao and
               Makoto Yamada and
               Louis{-}Philippe Morency and
               Ruslan Salakhutdinov},
  title     = {Neural Methods for Point-wise Dependency Estimation},
  booktitle = {NeurIPS},
  year      = {2020},
  url       = {https://proceedings.neurips.cc/paper/2020/hash/00a03ec6533ca7f5c644d198d815329c-Abstract.html},
  timestamp = {Wed, 20 Apr 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/Tsai0YMS20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

2019 (10)

Héctor Climente-González, Chloé-Agathe Azencott, Samuel Kaski, & Makoto Yamada. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data. Bioinform., 35(14): i427–i435. 2019.

Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data [link]

Paper doi link bibtex 1 download

@article{DBLP:journals/bioinformatics/Climente-Gonzalez19,
  author    = {H{\'{e}}ctor Climente{-}Gonz{\'{a}}lez and
               Chlo{\'{e}}{-}Agathe Azencott and
               Samuel Kaski and
               Makoto Yamada},
  title     = {Block {HSIC} Lasso: model-free biomarker detection for ultra-high
               dimensional data},
  journal   = {Bioinform.},
  volume    = {35},
  number    = {14},
  pages     = {i427--i435},
  year      = {2019},
  url       = {https://doi.org/10.1093/bioinformatics/btz333},
  doi       = {10.1093/bioinformatics/btz333},
  timestamp = {Mon, 26 Oct 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/bioinformatics/Climente-Gonzalez19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Heewon Park, Makoto Yamada, Seiya Imoto, & Satoru Miyano. Robust Sample-Specific Stability Selection with Effective Error Control. J. Comput. Biol., 26(3): 202–217. 2019.

Robust Sample-Specific Stability Selection with Effective Error Control [link]

Paper doi link bibtex

@article{DBLP:journals/jcb/ParkYIM19,
  author    = {Heewon Park and
               Makoto Yamada and
               Seiya Imoto and
               Satoru Miyano},
  title     = {Robust Sample-Specific Stability Selection with Effective Error Control},
  journal   = {J. Comput. Biol.},
  volume    = {26},
  number    = {3},
  pages     = {202--217},
  year      = {2019},
  url       = {https://doi.org/10.1089/cmb.2018.0180},
  doi       = {10.1089/cmb.2018.0180},
  timestamp = {Mon, 15 Jun 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/jcb/ParkYIM19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Learning to Sample Hard Instances for Graph Algorithms. In ACML, 2019.

Learning to Sample Hard Instances for Graph Algorithms [link]

Paper link bibtex

@inproceedings{DBLP:conf/acml/SatoYK19,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Learning to Sample Hard Instances for Graph Algorithms},
  booktitle = {ACML},
  year      = {2019},
  url       = {http://proceedings.mlr.press/v101/sato19a.html},
  timestamp = {Mon, 18 Nov 2019 16:57:55 +0100},
  biburl    = {https://dblp.org/rec/conf/acml/SatoYK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada, Louis-Philippe Morency, & Ruslan Salakhutdinov. Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel. In EMNLP, 2019.

Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/emnlp/TsaiBYMS19,
  author    = {Yao{-}Hung Hubert Tsai and
               Shaojie Bai and
               Makoto Yamada and
               Louis{-}Philippe Morency and
               Ruslan Salakhutdinov},
  title     = {Transformer Dissection: An Unified Understanding for Transformer's
               Attention via the Lens of Kernel},
  booktitle = {EMNLP},
  year      = {2019},
  url       = {https://doi.org/10.18653/v1/D19-1443},
  doi       = {10.18653/v1/D19-1443},
  timestamp = {Thu, 07 Apr 2022 09:14:07 +0200},
  biburl    = {https://dblp.org/rec/conf/emnlp/TsaiBYMS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, & Kenji Fukumizu. Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. In ICLR, 2019.

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator [link]

Paper link bibtex

@inproceedings{DBLP:conf/iclr/YamadaWTOSTF19,
  author    = {Makoto Yamada and
               Denny Wu and
               Yao{-}Hung Hubert Tsai and
               Hirofumi Ohta and
               Ruslan Salakhutdinov and
               Ichiro Takeuchi and
               Kenji Fukumizu},
  title     = {Post Selection Inference with Incomplete Maximum Mean Discrepancy
               Estimator},
  booktitle = {ICLR},
  year      = {2019},
  url       = {https://openreview.net/forum?id=BkG5SjR5YQ},
  timestamp = {Thu, 25 Jul 2019 13:03:15 +0200},
  biburl    = {https://dblp.org/rec/conf/iclr/YamadaWTOSTF19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Kiyosato Someya, Yuichi Hiroi, Makoto Yamada, & Yuta Itoh. OSTNet: Calibration Method for Optical See-Through Head-Mounted Displays via Non-Parametric Distortion Map Generation. In ISMAR, 2019.

OSTNet: Calibration Method for Optical See-Through Head-Mounted Displays via Non-Parametric Distortion Map Generation [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/ismar/SomeyaHY019,
  author    = {Kiyosato Someya and
               Yuichi Hiroi and
               Makoto Yamada and
               Yuta Itoh},
  title     = {OSTNet: Calibration Method for Optical See-Through Head-Mounted Displays
               via Non-Parametric Distortion Map Generation},
  booktitle = {ISMAR},
  year      = {2019},
  url       = {https://doi.org/10.1109/ISMAR-Adjunct.2019.00-34},
  doi       = {10.1109/ISMAR-Adjunct.2019.00-34},
  timestamp = {Wed, 05 Feb 2020 12:25:00 +0100},
  biburl    = {https://dblp.org/rec/conf/ismar/SomeyaHY019.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Daiki Tanaka, Makoto Yamada, Hisashi Kashima, Takeshi Kishikawa, Tomoyuki Haga, & Takamitsu Sasaki. In-Vehicle Network Intrusion Detection and Explanation Using Density Ratio Estimation. In ITSC, 2019.

In-Vehicle Network Intrusion Detection and Explanation Using Density Ratio Estimation [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/itsc/TanakaYKKHS19,
  author    = {Daiki Tanaka and
               Makoto Yamada and
               Hisashi Kashima and
               Takeshi Kishikawa and
               Tomoyuki Haga and
               Takamitsu Sasaki},
  title     = {In-Vehicle Network Intrusion Detection and Explanation Using Density
               Ratio Estimation},
  booktitle = {ITSC},
  year      = {2019},
  url       = {https://doi.org/10.1109/ITSC.2019.8917300},
  doi       = {10.1109/ITSC.2019.8917300},
  timestamp = {Mon, 09 Dec 2019 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/itsc/TanakaYKKHS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, & Wittawat Jitkrittum. Kernel Stein Tests for Multiple Model Comparison. In NeurIPS, 2019.

Kernel Stein Tests for Multiple Model Comparison [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/LimYSJ19,
  author    = {Jen Ning Lim and
               Makoto Yamada and
               Bernhard Sch{\"{o}}lkopf and
               Wittawat Jitkrittum},
  title     = {Kernel Stein Tests for Multiple Model Comparison},
  booktitle = {NeurIPS},
  year      = {2019},
  url       = {https://proceedings.neurips.cc/paper/2019/hash/bb7946e7d85c81a9e69fee1cea4a087c-Abstract.html},
  timestamp = {Mon, 16 May 2022 15:41:51 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/LimYSJ19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Ryoma Sato, Makoto Yamada, & Hisashi Kashima. Approximation Ratios of Graph Neural Networks for Combinatorial Problems. In NeurIPS, 2019.

Approximation Ratios of Graph Neural Networks for Combinatorial Problems [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/SatoYK19,
  author    = {Ryoma Sato and
               Makoto Yamada and
               Hisashi Kashima},
  title     = {Approximation Ratios of Graph Neural Networks for Combinatorial Problems},
  booktitle = {NeurIPS},
  year      = {2019},
  url       = {https://proceedings.neurips.cc/paper/2019/hash/635440afdfc39fe37995fed127d7df4f-Abstract.html},
  timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/SatoYK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Tam Le, Makoto Yamada, Kenji Fukumizu, & Marco Cuturi. Tree-Sliced Variants of Wasserstein Distances. In NeurIPS, 2019.

Tree-Sliced Variants of Wasserstein Distances [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/LeYFC19,
  author    = {Tam Le and
               Makoto Yamada and
               Kenji Fukumizu and
               Marco Cuturi},
  title     = {Tree-Sliced Variants of Wasserstein Distances},
  booktitle = {NeurIPS},
  year      = {2019},
  url       = {https://proceedings.neurips.cc/paper/2019/hash/2d36b5821f8affc6868b59dfc9af6c9f-Abstract.html},
  timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/LeYFC19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

2018 (7)

Nao Nitta, Takeaki Sugimura, Akihiro Isozaki, Hideharu Mikami, Kei Hiraki, Shinya Sakuma, Takanori Iino, Fumihito Arai, Taichiro Endo, Yasuhiro Fujiwaki, & others. Intelligent image-activated cell sorting. Cell, 175(1): 266–276. 2018.
link bibtex

@article{nitta2018intelligent,
  title={Intelligent image-activated cell sorting},
  author={Nitta, Nao and Sugimura, Takeaki and Isozaki, Akihiro and Mikami, Hideharu and Hiraki, Kei and Sakuma, Shinya and Iino, Takanori and Arai, Fumihito and Endo, Taichiro and Fujiwaki, Yasuhiro and others},
  journal={Cell},
  volume={175},
  number={1},
  pages={266--276},
  year={2018},
  publisher={Elsevier}
}

Kishan Wimalawarne, Makoto Yamada, & Hiroshi Mamitsuka. Convex Coupled Matrix and Tensor Completion. Neural Comput., 30(11). 2018.

Convex Coupled Matrix and Tensor Completion [link]

Paper doi link bibtex 1 download

@article{DBLP:journals/neco/WimalawarneYM18,
  author    = {Kishan Wimalawarne and
               Makoto Yamada and
               Hiroshi Mamitsuka},
  title     = {Convex Coupled Matrix and Tensor Completion},
  journal   = {Neural Comput.},
  volume    = {30},
  number    = {11},
  year      = {2018},
  url       = {https://doi.org/10.1162/neco\_a\_01123},
  doi       = {10.1162/neco\_a\_01123},
  timestamp = {Tue, 01 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/neco/WimalawarneYM18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez, Ermin Hodzic, Raunak Shrestha, Avishek Saha, Hua Ouyang, Dawei Yin, Hiroshi Mamitsuka, Süleyman Cenk Sahinalp, Predrag Radivojac, Filippo Menczer, & Yi Chang. Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data. IEEE Trans. Knowl. Data Eng., 30(7): 1352–1365. 2018.

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data [link]

Paper doi link bibtex 1 download

@article{DBLP:journals/tkde/YamadaTLHSSOYMS18,
  author    = {Makoto Yamada and
               Jiliang Tang and
               Jose Lugo{-}Martinez and
               Ermin Hodzic and
               Raunak Shrestha and
               Avishek Saha and
               Hua Ouyang and
               Dawei Yin and
               Hiroshi Mamitsuka and
               S{\"{u}}leyman Cenk Sahinalp and
               Predrag Radivojac and
               Filippo Menczer and
               Yi Chang},
  title     = {Ultra High-Dimensional Nonlinear Feature Selection for Big Biological
               Data},
  journal   = {{IEEE} Trans. Knowl. Data Eng.},
  volume    = {30},
  number    = {7},
  pages     = {1352--1365},
  year      = {2018},
  url       = {https://doi.org/10.1109/TKDE.2018.2789451},
  doi       = {10.1109/TKDE.2018.2789451},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/tkde/YamadaTLHSSOYMS18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Yue Wang, Dawei Yin, Luo Jie, Pengyuan Wang, Makoto Yamada, Yi Chang, & Qiaozhu Mei. Optimizing Whole-Page Presentation for Web Search. ACM Trans. Web, 12(3): 19:1–19:25. 2018.

Paper doi link bibtex

@article{DBLP:journals/tweb/WangYJWYCM18,
  author    = {Yue Wang and
               Dawei Yin and
               Luo Jie and
               Pengyuan Wang and
               Makoto Yamada and
               Yi Chang and
               Qiaozhu Mei},
  title     = {Optimizing Whole-Page Presentation for Web Search},
  journal   = {{ACM} Trans. Web},
  volume    = {12},
  number    = {3},
  pages     = {19:1--19:25},
  year      = {2018},
  url       = {https://doi.org/10.1145/3204461},
  doi       = {10.1145/3204461},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/tweb/WangYJWYCM18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Makoto Yamada, Yuta Umezu, Kenji Fukumizu, & Ichiro Takeuchi. Post Selection Inference with Kernels. In AISTATS, 2018.

Post Selection Inference with Kernels [link]

Paper link bibtex

@inproceedings{DBLP:conf/aistats/YamadaUFT18,
  author    = {Makoto Yamada and
               Yuta Umezu and
               Kenji Fukumizu and
               Ichiro Takeuchi},
  title     = {Post Selection Inference with Kernels},
  booktitle = {AISTATS},
  year      = {2018},
  url       = {http://proceedings.mlr.press/v84/yamada18a.html},
  timestamp = {Wed, 03 Apr 2019 18:17:22 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/YamadaUFT18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Tanmoy Mukherjee, Makoto Yamada, & Timothy M. Hospedales. Learning Unsupervised Word Translations Without Adversaries. In EMNLP, 2018.

Learning Unsupervised Word Translations Without Adversaries [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/emnlp/MukherjeeYH18,
  author    = {Tanmoy Mukherjee and
               Makoto Yamada and
               Timothy M. Hospedales},
  title     = {Learning Unsupervised Word Translations Without Adversaries},
  booktitle = {EMNLP},
  year      = {2018},
  url       = {https://doi.org/10.18653/v1/d18-1063},
  doi       = {10.18653/v1/d18-1063},
  timestamp = {Fri, 06 Aug 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/emnlp/MukherjeeYH18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Tam Le, & Makoto Yamada. Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams. In NeurIPS, 2018.

Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams [link]

Paper link bibtex

@inproceedings{DBLP:conf/nips/LeY18,
  author    = {Tam Le and
               Makoto Yamada},
  title     = {Persistence Fisher Kernel: {A} Riemannian Manifold Kernel for Persistence
               Diagrams},
  booktitle = {NeurIPS},
  year      = {2018},
  url       = {https://proceedings.neurips.cc/paper/2018/hash/959ab9a0695c467e7caf75431a872e5c-Abstract.html},
  timestamp = {Mon, 16 May 2022 15:41:51 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/LeY18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

2017 (2)

Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, & Samuel Kaski. Localized Lasso for High-Dimensional Regression. In AISTATS, 2017.

Localized Lasso for High-Dimensional Regression [link]

Paper link bibtex

@inproceedings{DBLP:conf/aistats/YamadaTISK17,
  author    = {Makoto Yamada and
               Koh Takeuchi and
               Tomoharu Iwata and
               John Shawe{-}Taylor and
               Samuel Kaski},
  title     = {Localized Lasso for High-Dimensional Regression},
  booktitle = {AISTATS},
  year      = {2017},
  url       = {http://proceedings.mlr.press/v54/yamada17a.html},
  timestamp = {Wed, 29 May 2019 08:41:44 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/YamadaTISK17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, & Yi Chang. Convex Factorization Machine for Toxicogenomics Prediction. In KDD, 2017.

Convex Factorization Machine for Toxicogenomics Prediction [link]

Paper doi link bibtex

@inproceedings{DBLP:conf/kdd/YamadaLGCWKKMC17,
  author    = {Makoto Yamada and
               Wenzhao Lian and
               Amit Goyal and
               Jianhui Chen and
               Kishan Wimalawarne and
               Suleiman A. Khan and
               Samuel Kaski and
               Hiroshi Mamitsuka and
               Yi Chang},
  title     = {Convex Factorization Machine for Toxicogenomics Prediction},
  booktitle = {KDD},
  year      = {2017},
  url       = {https://doi.org/10.1145/3097983.3098103},
  doi       = {10.1145/3097983.3098103},
  timestamp = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/kdd/YamadaLGCWKKMC17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}