Issei Sato

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 121

Top Venues

CoRR

ICML

AISTATS

NeurIPS

Publications

Tokio Kajitsuka, Issei Sato
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? ICLR (2024)
Soma Yokoi, Issei Sato
Top-Down Bayesian Posterior Sampling for Sum-Product Networks. KDD (2024)
Naoya Hasegawa, Issei Sato
Exploring Weight Balancing on Long-Tailed Recognition Problem. ICLR (2024)
Keitaro Sakamoto, Issei Sato
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training. CoRR (2024)
Akiyoshi Tomihari, Issei Sato
Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective. CoRR (2024)
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)
Soma Yokoi, Issei Sato
Top-Down Bayesian Posterior Sampling for Sum-Product Networks. CoRR (2024)
Naoya Hasegawa, Issei Sato
Exploring Weight Balancing on Long-Tailed Recognition Problem. CoRR (2023)
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)
Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka, Issei Sato
Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos. CoRR (2023)
Takeshi Koshizuka, Issei Sato
Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics. ICLR (2023)
Shuo Wang, Issei Sato
Understanding Parameter Saliency via Extreme Value Theory. CoRR (2023)
Tokio Kajitsuka, Issei Sato
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? CoRR (2023)
Kento Nozawa, Issei Sato
Evaluation Methods for Representation Learning: A Survey. IJCAI (2022)
Seiya Tokui, Issei Sato
Disentanglement Analysis with Partial Information Decomposition. ICLR (2022)
Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR (2022)
Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS (2022)
Keitaro Sakamoto, Issei Sato
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective. CoRR (2022)
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. ICML (2022)
Takahiro Suzuki, Shouhei Hanaoka, Issei Sato
Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection. CoRR (2022)
Keitaro Sakamoto, Issei Sato
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective. NeurIPS (2022)
Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS (2022)
Kento Nozawa, Issei Sato
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey. CoRR (2022)
Mingcheng Hou, Issei Sato
A Closer Look at Prototype Classifier for Few-shot Image Classification. NeurIPS (2022)
Takeshi Koshizuka, Issei Sato
Neural Lagrangian Schrödinger Bridge. CoRR (2022)
Zhenghang Cui, Issei Sato
Active Classification With Uncertainty Comparison Queries. Neural Comput. 34 (3) (2022)
Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato
Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices. Entropy 23 (8) (2021)
Kenshin Abe, Takanori Maehara, Issei Sato
Abelian Neural Networks. CoRR (2021)
Naoki Kobayashi, Taro Sekiyama, Issei Sato, Hiroshi Unno
Toward Neural-Network-Guided Program Synthesis and Verification. SAS (2021)
Mingcheng Hou, Issei Sato
A Closer Look at Prototype Classifier for Few-shot Image Classification. CoRR (2021)
Zeke Xie, Issei Sato, Masashi Sugiyama
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. ICLR (2021)
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) (2021)
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 (2021)
Yonghao Yue, Yuki Koyama, Issei Sato, Takeo Igarashi
User interfaces for high-dimensional design problems: from theories to implementations. SIGGRAPH Courses (2021)
Masahiro Fujisawa, Issei Sato
Multilevel Monte Carlo Variational Inference. J. Mach. Learn. Res. 22 (2021)
Kento Nozawa, Issei Sato
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. NeurIPS (2021)
Kento Nozawa, Issei Sato
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. CoRR (2021)
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)
Naoki Kobayashi, Taro Sekiyama, Issei Sato, Hiroshi Unno
Toward Neural-Network-Guided Program Synthesis and Verification. CoRR (2021)
Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR (2021)
Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33 (12) (2021)
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) (2021)
Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML (2021)
Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33 (5) (2021)
Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. AISTATS (2021)
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) (2021)
Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS (2021)
Seiya Tokui, Issei Sato
Disentanglement Analysis with Partial Information Decomposition. CoRR (2021)
Soma Yokoi, Takuma Otsuka, Issei Sato
Weak approximation of transformed stochastic gradient MCMC. Mach. Learn. 109 (9-10) (2020)
Yuki Koyama, Issei Sato, Masataka Goto
Sequential Gallery for Interactive Visual Design Optimization. CoRR (2020)
Kei Mukaiyama, Issei Sato, Masashi Sugiyama
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning. CoRR (2020)
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) (2020)
Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
Classification from Triplet Comparison Data. Neural Comput. 32 (3) (2020)
Futoshi Futami, Issei Sato, Masashi Sugiyama
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. ICML (2020)
Zeke Xie, Issei Sato, Masashi Sugiyama
A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast. CoRR (2020)
Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami, Issei Sato, Junya Honda, Masashi Sugiyama
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. CoRR (2020)
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) (2020)
Yuki Koyama, Issei Sato, Masataka Goto
Sequential gallery for interactive visual design optimization. ACM Trans. Graph. 39 (4) (2020)
Masahiro Fujisawa, Takeshi Teshima, Issei Sato
γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator. CoRR (2020)
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia. CoRR (2020)
Zeke Xie, Issei Sato, Masashi Sugiyama
Stable Weight Decay Regularization. CoRR (2020)
Takeshi Teshima, Issei Sato, Masashi Sugiyama
Few-shot Domain Adaptation by Causal Mechanism Transfer. CoRR (2020)
Zhenghang Cui, Issei Sato
Classification from Ambiguity Comparisons. CoRR (2020)
Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. CoRR (2020)
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 (2020)
Takeshi Teshima, Issei Sato, Masashi Sugiyama
Few-shot Domain Adaptation by Causal Mechanism Transfer. ICML (2020)
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)
Mantaro Yamada, Hiroaki Adachi, Ryoichi Horisaki, Issei Sato
A Comparison Of Compressed Sensing And Dnn Based Reconstruction For Ghost Motion Imaging. ICIP (2020)
Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR (2020)
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 (2020)
Yusuke Tsuzuku, Issei Sato
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions. CVPR (2019)
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis. CoRR (2019)
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI (2019)
Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR (2019)
Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama
Clipped Matrix Completion: A Remedy for Ceiling Effects. AAAI (2019)
Masahiro Fujisawa, Issei Sato
Multi-level Monte Carlo Variational Inference. CoRR (2019)
Xi Yang, Bojian Wu, Issei Sato, Takeo Igarashi
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping. CVPR Workshops (2019)
Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
Classification from Triplet Comparison Data. CoRR (2019)
Issei Sato
On Learning from Ghost Imaging without Imaging. CoRR (2019)
Xi Yang, Bojian Wu, Issei Sato, Takeo Igarashi
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping. CoRR (2019)
Kenshin Abe, Zijian Xu, Issei Sato, Masashi Sugiyama
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge. CoRR (2019)
Toby Long Hin Chong, I-Chao Shen, Issei Sato, Takeo Igarashi
Interactive Subspace Exploration on Generative Image Modelling. CoRR (2019)
Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR (2019)
Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama
Bayesian Posterior Approximation via Greedy Particle Optimization. AAAI (2019)
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 (2019)
Takuo Kaneko, Issei Sato, Masashi Sugiyama
Online Multiclass Classification Based on Prediction Margin for Partial Feedback. CoRR (2019)
Kento Nozawa, Issei Sato
PAC-Bayes Analysis of Sentence Representation. CoRR (2019)
Soma Yokoi, Issei Sato
Bayesian interpretation of SGD as Ito process. CoRR (2019)
Soma Yokoi, Takuma Otsuka, Issei Sato
On Transformations in Stochastic Gradient MCMC. CoRR (2019)
Yusuke Tsuzuku, Issei Sato
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions. CoRR (2018)
Hideaki Imamura, Issei Sato, Masashi Sugiyama
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. ICML (2018)
Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama
Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML (2018)
Hideaki Imamura, Issei Sato, Masashi Sugiyama
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. CoRR (2018)
Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105 (2018)
Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. AISTATS (2018)
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. NeurIPS (2018)
Futoshi Futami, Issei Sato, Masashi Sugiyama
Variational Inference based on Robust Divergences. AISTATS (2018)
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 (2018)
Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama
Variational Inference for Gaussian Processes with Panel Count Data. UAI (2018)
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Issei Sato