Sato, Issei

56 publications

ICML 2025 Benign Overfitting in Token Selection of Attention Mechanism Keitaro Sakamoto, Issei Sato

ICLR 2025 Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment Naoya Hasegawa, Issei Sato

ICML 2025 On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding Kevin Xu, Issei Sato

ICLR 2025 On the Optimal Memorization Capacity of Transformers Tokio Kajitsuka, Issei Sato

TMLR 2025 Prior Specification for Exposure-Based Bayesian Matrix Factorization Zicong Zhu, Issei Sato

NeurIPS 2025 Understanding Generalization in Physics Informed Models Through Affine Variety Dimensions Takeshi Koshizuka, Issei Sato

ICLR 2024 Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? Tokio Kajitsuka, Issei Sato

TMLR 2024 End-to-End Training Induces Information Bottleneck Through Layer-Role Differentiation: A Comparative Analysis with Layer-Wise Training Keitaro Sakamoto, Issei Sato

ICLR 2024 Exploring Weight Balancing on Long-Tailed Recognition Problem Naoya Hasegawa, Issei Sato

NeurIPS 2024 Understanding Linear Probing Then Fine-Tuning Language Models from NTK Perspective Akiyoshi Tomihari, Issei Sato

NeurIPS 2024 Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka, Issei Sato

ICLR 2023 Neural Lagrangian Schr\"odinger Bridge: Diffusion Modeling for Population Dynamics Takeshi Koshizuka, Issei Sato

NeurIPS 2023 On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama

AISTATS 2022 Pairwise Supervision Can Provably Elicit a Decision Boundary Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama

AISTATS 2022 Predictive Variational Bayesian Inference as Risk-Seeking Optimization Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama

NeurIPS 2022 A Closer Look at Prototype Classifier for Few-Shot Image Classification Mingcheng Hou, Issei Sato

ICML 2022 Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

NeurIPS 2022 Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective Keitaro Sakamoto, Issei Sato

ICLR 2022 Disentanglement Analysis with Partial Information Decomposition Seiya Tokui, Issei Sato

IJCAI 2022 Evaluation Methods for Representation Learning: A Survey Kento Nozawa, Issei Sato

AISTATS 2021 Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato

AISTATS 2021 Γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama

ICLR 2021 A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima Zeke Xie, Issei Sato, Masashi Sugiyama

ICML 2021 Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama

NeurIPS 2021 Loss Function Based Second-Order Jensen Inequality and Its Application to Particle Variational Inference Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama

JMLR 2021 Multilevel Monte Carlo Variational Inference Masahiro Fujisawa, Issei Sato

NeurIPS 2021 Understanding Negative Samples in Instance Discriminative Self-Supervised Representation Learning Kento Nozawa, Issei Sato

ICML 2020 Accelerating the Diffusion-Based Ensemble Sampling by Non-Reversible Dynamics Futoshi Futami, Issei Sato, Masashi Sugiyama

ICML 2020 Few-Shot Domain Adaptation by Causal Mechanism Transfer Takeshi Teshima, Issei Sato, Masashi Sugiyama

ICML 2020 Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama

MLJ 2020 Weak Approximation of Transformed Stochastic Gradient MCMC Soma Yokoi, Takuma Otsuka, Issei Sato

AAAI 2019 Bayesian Posterior Approximation via Greedy Particle Optimization Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama

AAAI 2019 Clipped Matrix Completion: A Remedy for Ceiling Effects Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama

CVPRW 2019 Directing DNNs Attention for Facial Attribution Classification Using Gradient-Weighted Class Activation Mapping Xi Yang, Bojian Wu, Issei Sato, Takeo Igarashi

AAAI 2019 Unsupervised Domain Adaptation Based on Source-Guided Discrepancy Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama

ICML 2018 Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model Hideaki Imamura, Issei Sato, Masashi Sugiyama

AISTATS 2018 Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama

ICML 2018 Does Distributionally Robust Supervised Learning Give Robust Classifiers? Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama

NeurIPS 2018 Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama

AISTATS 2018 Variational Inference Based on Robust Divergences Futoshi Futami, Issei Sato, Masashi Sugiyama

UAI 2018 Variational Inference for Gaussian Processes with Panel Count Data Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama

ACML 2017 A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors Zeke Xie, Issei Sato

JMLR 2017 Averaged Collapsed Variational Bayes Inference Katsuhiko Ishiguro, Issei Sato, Naonori Ueda

ICML 2017 Evaluating the Variance of Likelihood-Ratio Gradient Estimators Seiya Tokui, Issei Sato

NeurIPS 2017 Expectation Propagation for T-Exponential Family Using Q-Algebra Futoshi Futami, Issei Sato, Masashi Sugiyama

NeurIPS 2017 On the Model Shrinkage Effect of Gamma Process Edge Partition Models Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura

NeurIPS 2016 Differential Privacy Without Sensitivity Kentaro Minami, HItomi Arai, Issei Sato, Hiroshi Nakagawa

AAAI 2016 Infinite Plaid Models for Infinite Bi-Clustering Katsuhiko Ishiguro, Issei Sato, Masahiro Nakano, Akisato Kimura, Naonori Ueda

AAAI 2015 The Hybrid Nested/Hierarchical Dirichlet Process and Its Application to Topic Modeling with Word Differentiation Tengfei Ma, Issei Sato, Hiroshi Nakagawa

NeurIPS 2014 Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity than MAP Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi

ICML 2014 Approximation Analysis of Stochastic Gradient Langevin Dynamics by Using Fokker-Planck Equation and Ito Process Issei Sato, Hiroshi Nakagawa

ICML 2014 Latent Confusion Analysis by Normalized Gamma Construction Issei Sato, Hisashi Kashima, Hiroshi Nakagawa

ACML 2013 Multi-Armed Bandit Problem with Lock-up Periods Junpei Komiyama, Issei Sato, Hiroshi Nakagawa

ICML 2012 Rethinking Collapsed Variational Bayes Inference for LDA Issei Sato, Hiroshi Nakagawa

NeurIPS 2010 Deterministic Single-Pass Algorithm for LDA Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa

UAI 2009 Quantum Annealing for Variational Bayes Inference Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita