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2020 – today
- 2024
[j21]Keitaro Sakamoto, Issei Sato:
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training. Trans. Mach. Learn. Res. 2024 (2024)- [c77]Naoya Hasegawa, Issei Sato:
Exploring Weight Balancing on Long-Tailed Recognition Problem. ICLR 2024 - [c76]Tokio Kajitsuka, Issei Sato:
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? ICLR 2024 - [c75]Soma Yokoi
, Issei Sato:
Top-Down Bayesian Posterior Sampling for Sum-Product Networks. KDD 2024: 3977-3988 - [i60]Keitaro Sakamoto, Issei Sato:
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training. CoRR abs/2402.09050 (2024) - [i59]Akiyoshi Tomihari, Issei Sato:
Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective. CoRR abs/2405.16747 (2024) - [i58]Soma Yokoi, Issei Sato:
Top-Down Bayesian Posterior Sampling for Sum-Product Networks. CoRR abs/2406.12353 (2024) - [i57]Kevin Xu, Issei Sato:
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding. CoRR abs/2410.01405 (2024) - 2023
- [c74]Takeshi Koshizuka, Issei Sato:
Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics. ICLR 2023 - [c73]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 - [i56]Naoya Hasegawa, Issei Sato:
Exploring Weight Balancing on Long-Tailed Recognition Problem. CoRR abs/2305.16573 (2023) - [i55]Tokio Kajitsuka, Issei Sato:
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? CoRR abs/2307.14023 (2023) - [i54]Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka, Issei Sato:
Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos. CoRR abs/2310.06379 (2023) - [i53]Shuo Wang, Issei Sato:
Understanding Parameter Saliency via Extreme Value Theory. CoRR abs/2310.17951 (2023) - 2022
- [j20]Zhenghang Cui, Issei Sato:
Active Classification With Uncertainty Comparison Queries. Neural Comput. 34(3): 781-803 (2022) - [c72]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS 2022: 2618-2640 - [c71]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS 2022: 5051-5083 - [c70]Seiya Tokui, Issei Sato:
Disentanglement Analysis with Partial Information Decomposition. ICLR 2022 - [c69]Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama:
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. ICML 2022: 24430-24459 - [c68]Kento Nozawa, Issei Sato:
Evaluation Methods for Representation Learning: A Survey. IJCAI 2022: 5556-5563 - [c67]Mingcheng Hou, Issei Sato:
A Closer Look at Prototype Classifier for Few-shot Image Classification. NeurIPS 2022 - [c66]Keitaro Sakamoto, Issei Sato:
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective. NeurIPS 2022 - [i52]Takeshi Koshizuka, Issei Sato:
Neural Lagrangian Schrödinger Bridge. CoRR abs/2204.04853 (2022) - [i51]Kento Nozawa, Issei Sato:
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey. CoRR abs/2204.08226 (2022) - [i50]Takahiro Suzuki, Shouhei Hanaoka, Issei Sato:
Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection. CoRR abs/2204.13849 (2022) - [i49]Keitaro Sakamoto, Issei Sato:
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective. CoRR abs/2205.07320 (2022) - [i48]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR abs/2206.01606 (2022) - 2021
- [j19]Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, Issei Sato, Daisuke Sato, Naoto Hayashi, Osamu Abe:
Versatile anomaly detection method for medical images with semi-supervised flow-based generative models. Int. J. Comput. Assist. Radiol. Surg. 16(12): 2261-2267 (2021) - [j18]Toby Long Hin Chong, I-Chao Shen, Issei Sato, Takeo Igarashi:
Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content-based Guidance. Comput. Graph. Forum 40(1): 279-292 (2021) - [j17]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato:
Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices. Entropy 23(8): 993 (2021) - [j16]Masahiro Fujisawa, Issei Sato:
Multilevel Monte Carlo Variational Inference. J. Mach. Learn. Res. 22: 278:1-278:44 (2021) - [j15]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33(5): 1234-1268 (2021) - [j14]Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama:
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. Neural Comput. 33(8): 2163-2192 (2021) - [j13]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33(12): 3361-3412 (2021) - [c65]Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama:
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS 2021: 1783-1791 - [c64]Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato:
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. AISTATS 2021: 3664-3672 - [c63]Zeke Xie, Issei Sato, Masashi Sugiyama:
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. ICLR 2021 - [c62]Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML 2021: 7134-7144 - [c61]Kento Nozawa, Issei Sato:
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. NeurIPS 2021: 5784-5797 - [c60]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS 2021: 6803-6815 - [c59]Naoki Kobayashi, Taro Sekiyama, Issei Sato, Hiroshi Unno:
Toward Neural-Network-Guided Program Synthesis and Verification. SAS 2021: 236-260 - [c58]Yonghao Yue, Yuki Koyama, Issei Sato, Takeo Igarashi:
User interfaces for high-dimensional design problems: from theories to implementations. SIGGRAPH Courses 2021: 11:1-11:34 - [i47]Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR abs/2102.00678 (2021) - [i46]Kento Nozawa, Issei Sato:
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. CoRR abs/2102.06866 (2021) - [i45]Kenshin Abe, Takanori Maehara, Issei Sato:
Abelian Neural Networks. CoRR abs/2102.12232 (2021) - [i44]Naoki Kobayashi, Taro Sekiyama, Issei Sato, Hiroshi Unno:
Toward Neural-Network-Guided Program Synthesis and Verification. CoRR abs/2103.09414 (2021) - [i43]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. CoRR abs/2106.05010 (2021) - [i42]Seiya Tokui, Issei Sato:
Disentanglement Analysis with Partial Information Decomposition. CoRR abs/2108.13753 (2021) - [i41]Mingcheng Hou, Issei Sato:
A Closer Look at Prototype Classifier for Few-shot Image Classification. CoRR abs/2110.05076 (2021) - 2020
- [j12]Yukihiro Nomura, Soichiro Miki, Naoto Hayashi, Shouhei Hanaoka, Issei Sato, Takeharu Yoshikawa, Yoshitaka Masutani, Osamu Abe:
Novel platform for development, training, and validation of computer-assisted detection/diagnosis software. Int. J. Comput. Assist. Radiol. Surg. 15(4): 661-672 (2020) - [j11]Soma Yokoi, Takuma Otsuka, Issei Sato:
Weak approximation of transformed stochastic gradient MCMC. Mach. Learn. 109(9-10): 1903-1923 (2020) - [j10]Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Classification from Triplet Comparison Data. Neural Comput. 32(3): 659-681 (2020) - [j9]Yukihiro Nomura, Issei Sato, Toshihiro Hanawa, Shouhei Hanaoka, Takahiro Nakao, Tomomi Takenaga, Tetsuya Hoshino, Yuji Sekiya, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Osamu Abe:
Development of training environment for deep learning with medical images on supercomputer system based on asynchronous parallel Bayesian optimization. J. Supercomput. 76(9): 7315-7332 (2020) - [j8]Yuki Koyama, Issei Sato, Masataka Goto:
Sequential gallery for interactive visual design optimization. ACM Trans. Graph. 39(4): 88 (2020) - [c57]Mantaro Yamada, Hiroaki Adachi, Ryoichi Horisaki, Issei Sato:
A Comparison Of Compressed Sensing And Dnn Based Reconstruction For Ghost Motion Imaging. ICIP 2020: 2910-2914 - [c56]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. ICML 2020: 3337-3347 - [c55]Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. ICML 2020: 9458-9469 - [c54]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. ICML 2020: 9636-9647 - [i40]Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, Issei Sato, Naoto Hayashi, Osamu Abe:
Anomaly detection in chest radiographs with a weakly supervised flow-based deep learning method. CoRR abs/2001.07847 (2020) - [i39]Zeke Xie, Issei Sato, Masashi Sugiyama:
A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast. CoRR abs/2002.03495 (2020) - [i38]Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. CoRR abs/2002.03497 (2020) - [i37]Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami, Issei Sato, Junya Honda, Masashi Sugiyama:
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. CoRR abs/2003.04691 (2020) - [i36]Yuki Koyama, Issei Sato, Masataka Goto:
Sequential Gallery for Interactive Visual Design Optimization. CoRR abs/2005.04107 (2020) - [i35]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR abs/2006.06207 (2020) - [i34]Masahiro Fujisawa, Takeshi Teshima, Issei Sato:
γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator. CoRR abs/2006.07571 (2020) - [i33]Kei Mukaiyama, Issei Sato, Masashi Sugiyama:
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning. CoRR abs/2006.08306 (2020) - [i32]Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama:
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia. CoRR abs/2006.15815 (2020) - [i31]Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato:
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. CoRR abs/2007.01659 (2020) - [i30]Zhenghang Cui, Issei Sato:
Classification from Ambiguity Comparisons. CoRR abs/2008.00645 (2020) - [i29]Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama:
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. CoRR abs/2011.06220 (2020) - [i28]Zeke Xie, Issei Sato, Masashi Sugiyama:
Stable Weight Decay Regularization. CoRR abs/2011.11152 (2020)
2010 – 2019
- 2019
- [c53]Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Bayesian Posterior Approximation via Greedy Particle Optimization. AAAI 2019: 3606-3613 - [c52]Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama:
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI 2019: 4122-4129 - [c51]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: A Remedy for Ceiling Effects. AAAI 2019: 5151-5158 - [c50]Yusuke Tsuzuku, Issei Sato:
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions. CVPR 2019: 51-60 - [c49]Xi Yang, Bojian Wu, Issei Sato, Takeo Igarashi:
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping. CVPR Workshops 2019: 103-106 - [i27]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis. CoRR abs/1901.04653 (2019) - [i26]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR abs/1901.11351 (2019) - [i25]Masahiro Fujisawa, Issei Sato:
Multi-level Monte Carlo Variational Inference. CoRR abs/1902.00468 (2019) - [i24]Takuo Kaneko, Issei Sato, Masashi Sugiyama:
Online Multiclass Classification Based on Prediction Margin for Partial Feedback. CoRR abs/1902.01056 (2019) - [i23]Kento Nozawa, Issei Sato:
PAC-Bayes Analysis of Sentence Representation. CoRR abs/1902.04247 (2019) - [i22]Soma Yokoi, Takuma Otsuka, Issei Sato:
On Transformations in Stochastic Gradient MCMC. CoRR abs/1903.02750 (2019) - [i21]Issei Sato:
On Learning from Ghost Imaging without Imaging. CoRR abs/1903.06009 (2019) - [i20]Hiroaki Adachi, Yoko Kawamura, Keiji Nakagawa, Ryoichi Horisaki, Issei Sato, Satoko Yamaguchi, Katsuhito Fujiu, Kayo Waki, Hiroyuki Noji, Sadao Ota:
Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics. CoRR abs/1903.09538 (2019) - [i19]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR abs/1904.11717 (2019) - [i18]Xi Yang, Bojian Wu, Issei Sato, Takeo Igarashi:
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping. CoRR abs/1905.00593 (2019) - [i17]Kenshin Abe, Zijian Xu, Issei Sato, Masashi Sugiyama:
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge. CoRR abs/1905.11623 (2019) - [i16]Toby Long Hin Chong, I-Chao Shen, Issei Sato, Takeo Igarashi:
Interactive Subspace Exploration on Generative Image Modelling. CoRR abs/1906.09840 (2019) - [i15]Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Classification from Triplet Comparison Data. CoRR abs/1907.10225 (2019) - [i14]Soma Yokoi, Issei Sato:
Bayesian interpretation of SGD as Ito process. CoRR abs/1911.09011 (2019) - 2018
- [j7]Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Stochastic Divergence Minimization for Biterm Topic Models. IEICE Trans. Inf. Syst. 101-D(3): 668-677 (2018) - [j6]Yo Ehara, Issei Sato, Hidekazu Oiwa, Hiroshi Nakagawa:
Mining Words in the Minds of Second Language Learners for Learner-specific Word Difficulty. J. Inf. Process. 26: 267-275 (2018) - [j5]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105: 132-141 (2018) - [c48]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Variational Inference based on Robust Divergences. AISTATS 2018: 813-822 - [c47]Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama:
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. AISTATS 2018: 1108-1116 - [c46]Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama:
Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML 2018: 2034-2042 - [c45]Hideaki Imamura, Issei Sato, Masashi Sugiyama:
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. ICML 2018: 2152-2161 - [c44]Issei Sato, Yukihiro Nomura, Shouhei Hanaoka, Soichiro Miki, Naoto Hayashi, Osamu Abe, Yoshitaka Masutani:
Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibition. KDD 2018: 695-704 - [c43]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. NeurIPS 2018: 6542-6551 - [c42]Takashi Miyazaki, Issei Sato, Nobuyuki Shimizu:
Bayesian Optimization of HPC Systems for Energy Efficiency. ISC 2018: 44-62 - [c41]Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama:
Variational Inference for Gaussian Processes with Panel Count Data. UAI 2018: 290-299 - [i13]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. CoRR abs/1802.04034 (2018) - [i12]Hideaki Imamura, Issei Sato, Masashi Sugiyama:
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. CoRR abs/1802.04551 (2018) - [i11]Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Frank-Wolfe Stein Sampling. CoRR abs/1805.07912 (2018) - [i10]Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama:
Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR abs/1809.03839 (2018) - [i9]Yusuke Tsuzuku, Issei Sato:
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions. CoRR abs/1809.04098 (2018) - [i8]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: a Remedy for Ceiling Effects. CoRR abs/1809.04997 (2018) - 2017
- [j4]Katsuhiko Ishiguro, Issei Sato, Naonori Ueda:
Averaged Collapsed Variational Bayes Inference. J. Mach. Learn. Res. 18: 1:1-1:29 (2017) - [j3]Yuki Koyama, Issei Sato, Daisuke Sakamoto, Takeo Igarashi:
Sequential line search for efficient visual design optimization by crowds. ACM Trans. Graph. 36(4): 48:1-48:11 (2017) - [c40]Zeke Xie, Issei Sato:
A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors. ACML 2017: 81-96 - [c39]Seiya Tokui, Issei Sato:
Evaluating the Variance of Likelihood-Ratio Gradient Estimators. ICML 2017: 3414-3423 - [c38]Ryosuke Kamesawa, Issei Sato, Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto, Naoto Hayashi, Masashi Sugiyama:
Lung lesion detection in FDG-PET/CT with Gaussian process regression. Medical Imaging: Computer-Aided Diagnosis 2017: 101340C - [c37]Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura:
On the Model Shrinkage Effect of Gamma Process Edge Partition Models. NIPS 2017: 397-405 - [c36]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Expectation Propagation for t-Exponential Family Using q-Algebra. NIPS 2017: 2245-2254 - [i7]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR abs/1704.06767 (2017) - [i6]Zeke Xie, Issei Sato:
A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors. CoRR abs/1711.08117 (2017) - 2016
- [c35]Katsuhiko Ishiguro, Issei Sato, Masahiro Nakano, Akisato Kimura, Naonori Ueda:
Infinite Plaid Models for Infinite Bi-Clustering. AAAI 2016: 1701-1708 - [c34]Masahiro Kazama, Issei Sato, Haruaki Yatabe, Tairiku Ogihara, Tetsuro Onishi, Hiroshi Nakagawa:
Company recommendation for new graduates via implicit feedback multiple matrix factorization with Bayesian optimization. IEEE BigData 2016: 1615-1620 - [c33]Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Issei Sato:
Model-Based Approaches for Independence-Enhanced Recommendation. ICDM Workshops 2016: 860-867 - [c32]Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa:
Differential Privacy without Sensitivity. NIPS 2016: 956-964 - [i5]Seiya Tokui, Issei Sato:
Reparameterization trick for discrete variables. CoRR abs/1611.01239 (2016) - [i4]Weihua Hu, Issei Sato, Masashi Sugiyama:
Robust supervised learning under uncertainty in dataset shift. CoRR abs/1611.02041 (2016) - 2015
- [c31]Tengfei Ma, Issei Sato, Hiroshi Nakagawa:
The Hybrid Nested/Hierarchical Dirichlet Process and its Application to Topic Modeling with Word Differentiation. AAAI 2015: 2835-2841 - [c30]Issei Sato, Hiroshi Nakagawa:
Stochastic Divergence Minimization for Online Collapsed Variational Bayes Zero Inference of Latent Dirichlet Allocation. KDD 2015: 1035-1044 - [c29]Seiya Tokui, Issei Sato, Hiroshi Nakagawa:
Locally Optimized Hashing for Nearest Neighbor Search. PAKDD (2) 2015: 498-509 - [c28]Bin Yang, Issei Sato, Hiroshi Nakagawa:
Bayesian Differential Privacy on Correlated Data. SIGMOD Conference 2015: 747-762 - 2014
- [c27]Yo Ehara, Yusuke Miyao, Hidekazu Oiwa, Issei Sato, Hiroshi Nakagawa:
Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning. EMNLP 2014: 1374-1384 - [c26]Issei Sato, Hiroshi Nakagawa:
Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process. ICML 2014: 982-990 - [c25]Issei Sato, Hisashi Kashima, Hiroshi Nakagawa:
Latent Confusion Analysis by Normalized Gamma Construction. ICML 2014: 1116-1124 - [c24]Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi:
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. NIPS 2014: 1224-1232 - [i3]Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita:
Quantum Annealing for Variational Bayes Inference. CoRR abs/1408.2037 (2014) - [i2]Katsuhiko Ishiguro, Issei Sato, Naonori Ueda:
Collapsed Variational Bayes Inference of Infinite Relational Model. CoRR abs/1409.4757 (2014) - 2013
- [j2]Issei Sato, Shu Tanaka, Kenichi Kurihara, Seiji Miyashita, Hiroshi Nakagawa:
Quantum annealing for Dirichlet process mixture models with applications to network clustering. Neurocomputing 121: 523-531 (2013) - [c23]Junpei Komiyama, Issei Sato, Hiroshi Nakagawa:
Multi-armed Bandit Problem with Lock-up Periods. ACML 2013: 116-132 - [c22]Yo Ehara, Issei Sato, Hidekazu Oiwa, Hiroshi Nakagawa:
Understanding seed selection in bootstrapping. TextGraphs@EMNLP 2013: 44-52 - 2012
- [c21]Shingo Takamatsu, Issei Sato, Hiroshi Nakagawa:
Reducing Wrong Labels in Distant Supervision for Relation Extraction. ACL (1) 2012: 721-729 - [c20]Yo Ehara, Issei Sato, Hidekazu Oiwa, Hiroshi Nakagawa:
Mining Words in the Minds of Second Language Learners: Learner-Specific Word Difficulty. COLING 2012: 799-814 - [c19]Hiroshi Kajino, Yuta Tsuboi, Issei Sato, Hisashi Kashima:
Learning from Crowds and Experts. HCOMP@AAAI 2012 - [c18]Issei Sato, Hiroshi Nakagawa:
Rethinking Collapsed Variational Bayes Inference for LDA. ICML 2012 - [c17]Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa:
Practical collapsed variational bayes inference for hierarchical dirichlet process. KDD 2012: 105-113 - [c16]Bin Yang, Issei Sato, Hiroshi Nakagawa:
Privacy-Preserving EM Algorithm for Clustering on Social Network. PAKDD (1) 2012: 542-553 - 2011
- [c15]Bin Yang, Issei Sato, Hiroshi Nakagawa:
Secure Clustering in Private Networks. ICDM 2011: 894-903 - [c14]Shingo Takamatsu, Issei Sato, Hiroshi Nakagawa:
Probabilistic Matrix Factorization Leveraging Contexts for Unsupervised Relation Extraction. PAKDD (1) 2011: 87-99 - 2010
- [j1]Issei Sato, Hiroshi Nakagawa:
Succinct Semi-structured Data Mining Based on FREQT. Inf. Media Technol. 5(3): 1022-1027 (2010) - [c13]Minoru Yoshida, Shin Matsushima, Shingo Ono, Issei Sato, Hiroshi Nakagawa:
ITC-UT: Tweet Categorization by Query Categorization for On-line Reputation Management. CLEF (Notebook Papers/LABs/Workshops) 2010 - [c12]Bin Yang, Hiroshi Nakagawa, Issei Sato, Jun Sakuma:
Collusion-resistant privacy-preserving data mining. KDD 2010: 483-492 - [c11]Issei Sato, Hiroshi Nakagawa:
Topic models with power-law using Pitman-Yor process. KDD 2010: 673-682 - [c10]Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa:
Deterministic Single-Pass Algorithm for LDA. NIPS 2010: 2074-2082 - [c9]Minoru Yoshida, Issei Sato, Hiroshi Nakagawa, Akira Terada:
Mining Numbers in Text Using Suffix Arrays and Clustering Based on Dirichlet Process Mixture Models. PAKDD (2) 2010: 230-237 - [c8]Minoru Yoshida, Masaki Ikeda, Shingo Ono, Issei Sato, Hiroshi Nakagawa:
Person name disambiguation by bootstrapping. SIGIR 2010: 10-17
2000 – 2009
- 2009
- [c7]Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita:
Quantum Annealing for Variational Bayes Inference. UAI 2009: 479-486 - [i1]Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita:
Quantum Annealing for Variational Bayes Inference. CoRR abs/0905.3528 (2009) - 2008
- [c6]Issei Sato, Minoru Yoshida, Hiroshi Nakagawa:
Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model. KDD 2008: 587-595 - [c5]Shingo Ono, Issei Sato, Minoru Yoshida, Hiroshi Nakagawa:
Person Name Disambiguation in Web Pages Using Social Network, Compound Words and Latent Topics. PAKDD 2008: 260-271 - 2007
- [c4]Issei Sato, Hiroshi Nakagawa:
Bayesian Document Generative Model with Explicit Multiple Topics. EMNLP-CoNLL 2007: 421-429 - [c3]Issei Sato, Hiroshi Nakagawa:
Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior. KDD 2007: 590-598 - [c2]Issei Sato, Hiroshi Nakagawa:
Semi-structure Mining Method for Text Mining with a Chunk-Based Dependency Structure. PAKDD 2007: 777-784 - 2006
- [c1]Issei Sato, Yu Hirate, Hayato Yamana:
Text Mining using PrefixSpan constrained by Item Interval and Item Attribute. ICDE Workshops 2006: 118
Coauthor Index
[c73] [c72] [c71] [c69] [i48] [j15] [j14] [j13] [c65] [c63] [c62] [c60] [i47] [i43] [j10] [c56] [c55] [c54] [i39] [i38] [i37] [i35] [i33] [i32] [i29] [i28] [c53] [c52] [c51] [i27] [i26] [i24] [i19] [i17] [i15] [j7] [j5] [c48] [c47] [c46] [c45] [c43] [c41] [i13] [i12] [i11] [i10] [i8] [c38] [c36] [i7] [i4] [c24]
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