generated by bibbase.org
  2024 (11)
Pengfei He, Han Xu, Jie Ren, Yingqian Cui, Shenglai Zeng, Yue Xing, Jiliang Tang, Makoto Yamada, & Mohammad Sabokrou. Stealthy Backdoor Attack via Confidence-driven Sampling. Transactions on Machine Learning Research. 2024.
Stealthy Backdoor Attack via Confidence-driven Sampling [link]Paper   link   bibtex  
Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald, Deborah Sulem, Han Zhao, & Yao-Hung Hubert Tsai. An Empirical Study of Simplicial Representation Learning with Wasserstein Distance. Entropy. 2024.
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Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, & Makoto Yamada. Polyak Meets Parameter-free Clipped Gradient Descent. In NeurIPS, 2024.
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Hossein Mirzaei, Ali Ansari, Bahar Dibaei Nia, Mojtaba Nafez, Moein Madadi, Sepehr Rezaee, Zeinab Sadat Taghavi, Arad Maleki, Kian Shamsaie, Mahdi Hajialilue, Jafar Habibi, Mohammad Sabokrou, & Mohammad Hossein Rohban. Scanning Trojaned Models Using Out-of-Distribution Samples. In NeurIPS, 2024.
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Aditya Sinha, Siqi Zeng, Makoto Yamada, & Han Zhao. Learning Structured Representations with Hyperbolic Embeddings. In NeurIPS, 2024.
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Yuping Lin, Pengfei He, Han Xu, Yue Xing, Makoto Yamada, Hui Liu, & Jiliang Tang. Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis. In EMNLP, 2024.
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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.
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Mozhgan Pourkeshavarz, Mohammad Sabokrou, & Amir Rasouli. Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving. In CVPR, 2024.
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Guillaume Houry, Han Bao, Han Zhao, & Makoto Yamada. Fast 1-Wasserstein distance approximations using greedy strategies. In AISTATS, 2024.
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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.
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Peter Naylor, Diego Di Carlo, Arianna Traviglia, Makoto Yamada, & Marco Fiorucci. Implicit neural representation for change detection. In WACV, 2024.
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  2023 (14)
Sho Otao, & Makoto Yamada. A linear time approximation of Wasserstein distance with word embedding selection. In EMNLP, 2023.
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Cléa Laouar, Yuki Takezawa, & Makoto Yamada. Large-scale similarity search with Optimal Transport. In EMNLP, 2023.
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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.
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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   13 downloads  
Raha Ahmadi, Mohammad Javad Rajabi, Mohammad Khalooiem, & Mohamamd Sabokrou. Mitigating Bias: Enhancing Image Classification by Improving Model Explanations. In ACML, 2023.
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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.
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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   16 downloads  
Marco Fiorucci, Peter Naylor, & Makoto Yamada. Optimal Transport for Change Detection on LiDAR Point Clouds. IGARSS. 2023.
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Dinesh Singh, Héctor Climente-González, Mathis Petrovich, Eiryo Kawakami, & Makoto Yamada. Fsnet: Feature selection network on high-dimensional biological data. IJCNN. 2023.
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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  
Ryuichiro Hataya, & Makoto Yamada. Nyström Method for Accurate and Scalable Implicit Differentiation. In AISTATS, 2023.
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Weijie Liu, Jiahao Xie, Chao Zhang, Makoto Yamada, Nenggan Zheng, & Hui Qian. Robust Graph Dictionary Learning. In ICLR, 2023.
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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.
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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.
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  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.
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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   7 downloads  
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  
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   7 downloads  
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  
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  
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  
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  
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  
  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.
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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  
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  
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  
Vu Nguyen, Tam Le, Makoto Yamada, & Michael A. Osborne. Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. In ICML, 2021.
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search [link]Paper   link   bibtex  
Yuki Takezawa, Ryoma Sato, & Makoto Yamada. Supervised Tree-Wasserstein Distance. In ICML, 2021.
Supervised Tree-Wasserstein Distance [link]Paper   link   bibtex   4 downloads  
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  
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   1 download  
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  
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  
  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  
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  
Benjamin Poignard, & Makoto Yamada. Sparse Hilbert-Schmidt Independence Criterion Regression. In AISTATS, 2020.
Sparse Hilbert-Schmidt Independence Criterion Regression [link]Paper   link   bibtex  
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  
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  
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   1 download  
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  
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  
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   1 download  
  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  
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  
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  
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   1 download  
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  
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  
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  
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  
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  
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  
  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.
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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   2 downloads  
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  
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.
Optimizing Whole-Page Presentation for Web Search [link]Paper   doi   link   bibtex  
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  
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  
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  
  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  
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