Taiji Suzuki

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 158

Top Venues

Publications

Naoki Nishikawa, Taiji Suzuki
State Space Models are Comparable to Transformers in Estimating Functions with Dynamic Smoothness. CoRR (2024)
Juno Kim, Taiji Suzuki
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape. CoRR (2024)
Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. COLT (2024)
Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin M. Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Atsushi Yamashita, Michael Poli
State-Free Inference of State-Space Models: The Transfer Function Approach. CoRR (2024)
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki
Koopman-based generalization bound: New aspect for full-rank weights. ICLR (2024)
Yuto Nishimura, Taiji Suzuki
Minimax optimality of convolutional neural networks for infinite dimensional input-output problems and separation from kernel methods. ICLR (2024)
Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR (2024)
Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli
Mechanistic Design and Scaling of Hybrid Architectures. CoRR (2024)
Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. CoRR (2024)
Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama
Flow matching achieves minimax optimal convergence. CoRR (2024)
Keita Suzuki, Taiji Suzuki
Optimal criterion for feature learning of two-layer linear neural network in high dimensional interpolation regime. ICLR (2024)
Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems. ICLR (2024)
Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization. CoRR (2024)
Wei Huang, Ye Shi, Zhongyi Cai, Taiji Suzuki
Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. ICLR (2024)
Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré
How do Transformers perform In-Context Autoregressive Learning? CoRR (2024)
Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida, Chieh-Hsin Lai, Naoki Murata, Yuki Mitsufuji
Understanding Multimodal Contrastive Learning Through Pointwise Mutual Information. CoRR (2024)
Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. CoRR (2024)
Shokichi Takakura, Taiji Suzuki
Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective. CoRR (2024)
Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. CoRR (2024)
Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu
Gradient-Based Feature Learning under Structured Data. NeurIPS (2023)
Tomoya Murata, Taiji Suzuki
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. ICML (2023)
Taiji Suzuki, Denny Wu, Atsushi Nitanda
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. NeurIPS (2023)
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
Tight and fast generalization error bound of graph embedding in metric space. ICML (2023)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective. NeurIPS (2023)
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
Tight and fast generalization error bound of graph embedding in metric space. CoRR (2023)
Shuhei Nitta, Taiji Suzuki, Albert Rodríguez Mulet, Atsushi Yaguchi, Ryusuke Hirai
Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories. CoRR (2023)
Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. CoRR (2023)
Kazusato Oko, Shunta Akiyama, Taiji Suzuki
Diffusion Models are Minimax Optimal Distribution Estimators. ICML (2023)
Shunta Akiyama, Taiji Suzuki
Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. ICLR (2023)
Tomoya Murata, Taiji Suzuki
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. CoRR (2023)
Taiji Suzuki, Atsushi Nitanda, Denny Wu
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. ICLR (2023)
Shokichi Takakura, Taiji Suzuki
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input. ICML (2023)
Taiji Suzuki, Denny Wu, Atsushi Nitanda
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. CoRR (2023)
Shuhei Nitta, Taiji Suzuki, Albert Rodríguez Mulet, Atsushi Yaguchi, Ryusuke Hirai
Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories. ICMLA (2023)
Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS (2023)
Kishan Wimalawarne, Taiji Suzuki, Sophie Langer
Learning Green's Function Efficiently Using Low-Rank Approximations. CoRR (2023)
Shokichi Takakura, Taiji Suzuki
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input. CoRR (2023)
Kazusato Oko, Shunta Akiyama, Taiji Suzuki
Diffusion Models are Minimax Optimal Distribution Estimators. CoRR (2023)
Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. CoRR (2023)
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki
Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering. CoRR (2023)
Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu
Gradient-Based Feature Learning under Structured Data. CoRR (2023)
Hiroaki Kingetsu, Kenichi Kobayashi, Taiji Suzuki
Neural Network Module Decomposition and Recomposition with Superimposed Masks. IJCNN (2023)
Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. ICML (2023)
Kishan Wimalawarne, Taiji Suzuki
Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs. CoRR (2022)
Chenyuan Xu, Kosuke Haruki, Taiji Suzuki, Masahiro Ozawa, Kazuki Uematsu, Ryuji Sakai
Data-Parallel Momentum Diagonal Empirical Fisher (DP-MDEF):Adaptive Gradient Method is Affected by Hessian Approximation and Multi-Class Data. ICMLA (2022)
Yuri Kinoshita, Taiji Suzuki
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization. NeurIPS (2022)
Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki
Dimension-free convergence rates for gradient Langevin dynamics in RKHS. COLT (2022)
Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR (2022)
Kazusato Oko, Shunta Akiyama, Tomoya Murata, Taiji Suzuki
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning. CoRR (2022)
Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? ECML/PKDD (3) (2022)
Shunta Akiyama, Taiji Suzuki
Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. CoRR (2022)
Kengo Machida, Kuniaki Uto, Koichi Shinoda, Taiji Suzuki
MSR-DARTS: Minimum Stable Rank of Differentiable Architecture Search. IJCNN (2022)
Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang
Understanding the Variance Collapse of SVGD in High Dimensions. ICLR (2022)
Tomoya Murata, Taiji Suzuki
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. NeurIPS (2022)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. NeurIPS (2022)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. CoRR (2022)
Yuri Kinoshita, Taiji Suzuki
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization. CoRR (2022)
Sho Okumoto, Taiji Suzuki
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness. ICLR (2022)
Kishan Wimalawarne, Taiji Suzuki
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank. ACML (2022)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Convex Analysis of the Mean Field Langevin Dynamics. AISTATS (2022)
Tomoya Murata, Taiji Suzuki
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. CoRR (2022)
Chihiro Watanabe, Taiji Suzuki
Deep two-way matrix reordering for relational data analysis. Neural Networks 146 (2022)
Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. NeurIPS (2022)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Convex Analysis of the Mean Field Langevin Dynamics. CoRR (2022)
Shunta Akiyama, Taiji Suzuki
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. CoRR (2021)
Chihiro Watanabe, Taiji Suzuki
Goodness-of-fit Test on the Number of Biclusters in Relational Data Matrix. CoRR (2021)
Chihiro Watanabe, Taiji Suzuki
AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network. CoRR (2021)
Tomoya Murata, Taiji Suzuki
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. ICML (2021)
Atsushi Yaguchi, Taiji Suzuki, Shuhei Nitta, Yukinobu Sakata, Akiyuki Tanizawa
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks. IJCAI (2021)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. NeurIPS (2021)
Hiroaki Kingetsu, Kenichi Kobayashi, Taiji Suzuki
Neural Network Module Decomposition and Recomposition. CoRR (2021)
Taiji Suzuki, Shunta Akiyama
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. ICLR (2021)
Chihiro Watanabe, Taiji Suzuki
AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network. SSCI (2021)
Atsushi Nitanda, Tomoya Murata, Taiji Suzuki
Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization. Knowl. Inf. Syst. 63 (9) (2021)
Chihiro Watanabe, Taiji Suzuki
Goodness-of-fit test for latent block models. Comput. Stat. Data Anal. 154 (2021)
Atsushi Nitanda, Taiji Suzuki
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. ICLR (2021)
Kishan Wimalawarne, Taiji Suzuki
Adaptive and Interpretable Graph Convolution Networks Using Generalized Pagerank. CoRR (2021)
Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training. CoRR (2021)
Tomoya Murata, Taiji Suzuki
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. CoRR (2021)
Tomoya Murata, Taiji Suzuki
Gradient Descent in RKHS with Importance Labeling. AISTATS (2021)
Shingo Yashima, Atsushi Nitanda, Taiji Suzuki
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. AISTATS (2021)
Stefano Massaroli, Michael Poli, Sho Sonoda, Taiji Suzuki, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
Differentiable Multiple Shooting Layers. NeurIPS (2021)
Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
When does preconditioning help or hurt generalization? ICLR (2021)
Akira Nakagawa, Keizo Kato, Taiji Suzuki
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding. ICML (2021)
Shunta Akiyama, Taiji Suzuki
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. ICML (2021)
Chihiro Watanabe, Taiji Suzuki
Deep Two-way Matrix Reordering for Relational Data Analysis. CoRR (2021)
Taiji Suzuki, Atsushi Nitanda
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. NeurIPS (2021)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. ICLR (2020)
Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
Independently Interpretable Lasso for Generalized Linear Models. Neural Comput. 32 (6) (2020)
Kenta Oono, Taiji Suzuki
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks. CoRR (2020)
Taiji Suzuki
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics. CoRR (2020)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. CoRR (2020)
Atsushi Nitanda, Taiji Suzuki
Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. AISTATS (2020)
Kazuma Tsuji, Taiji Suzuki
Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space. CoRR (2020)
Kengo Machida, Kuniaki Uto, Koichi Shinoda, Taiji Suzuki
Neural Architecture Search Using Stable Rank of Convolutional Layers. CoRR (2020)
Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang
Understanding Generalization in Deep Learning via Tensor Methods. CoRR (2020)
Yusuke Hayashi, Taiji Suzuki
Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error. CoRR (2020)
Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
When Does Preconditioning Help or Hurt Generalization? CoRR (2020)
Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu
A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. Comput. Stat. Data Anal. 152 (2020)
Chihiro Watanabe, Taiji Suzuki
Selective Inference for Latent Block Models. CoRR (2020)

Taiji Suzuki.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Taiji Suzuki