Authors · raw publication identity
Taiji Suzuki
全分野 · 掲載済みの全期間（2010–2026年）
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対象会議のFull paperを全分野・掲載済み全期間でまとめています。1996～2025年の追加分には、氏名の一致・表記の正規化等に基づき同一人物と推定した対応を含みます。本人確認済みの対応や出版時所属の確認とは区別しています。表示する大学は著者ページの識別用で、過去論文の出版時所属や現在所属を示しません。追加分は大学ランキングやEdition別の件数に含みません。保存済みの1996～2025年の対象資料から確認できた範囲を追加しており、全業績の網羅を保証するものではありません。
著者ページの大学: 東京大学 · Top conference papers: 107
分野別内訳
分野別の本数はcross-listにより重なる場合があります。合計本数は同一論文を1本に数えます。
年別内訳
- 2026年 7
- 2025年 22
- 2024年 22
- 2023年 11
- 2022年 8
- 2021年 10
- 2020年 6
- 2019年 2
- 2018年 2
- 2017年 2
- 2016年 3
- 2015年 2
- 2014年 1
- 2013年 2
- 2012年 3
- 2011年 3
- 2010年 1
論文一覧（新しい年順）
- On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
- Transformers as Measure-Theoretic Associative Memory: A Statistical Perspective and Minimax Optimality
- In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
- Inference-Aware Meta-Alignment of LLMs via Non-Linear GRPO
- Inference-time Alignment with Rewards in Besov Spaces: Provable Advantages of Feature Learning and Multi-Step Policy Updates
- Provable Sample Efficiency of Curriculum Post-Training for Transformer Reasoning
- Zero-Flow Encoders
- Direct Distributional Optimization for Provable Alignment of Diffusion Models
- Flow matching achieves almost minimax optimal convergence
- On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
- Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
- State Space Models are Provably Comparable to Transformers in Dynamic Token Selection
- Transformers Provably Solve Parity Efficiently with Chain of Thought
- Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric
- Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
- Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
- Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
- Nonlinear transformers can perform inference-time feature learning
- On the Role of Label Noise in the Feature Learning Process
- Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble
- Provable In-Context Vector Arithmetic via Retrieving Task Concepts
- Quantifying Memory Utilization with Effective State-Size
- Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency
- From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers
- Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel
- Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
- How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
- State Size Independent Statistical Error Bound for Discrete Diffusion Models
- Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression
- Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
- Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data
- Koopman-based generalization bound: New aspect for full-rank weights
- Minimax optimality of convolutional neural networks for infinite dimensional input-output problems and separation from kernel methods
- Optimal criterion for feature learning of two-layer linear neural network in high dimensional interpolation regime
- Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
- Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory
- High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
- How do Transformers Perform In-Context Autoregressive Learning ?
- Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning
- Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
- Mechanistic Design and Scaling of Hybrid Architectures
- Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
- SILVER: Single-loop variance reduction and application to federated learning
- State-Free Inference of State-Space Models: The *Transfer Function* Approach
- Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
- Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
- On the Comparison between Multi-modal and Single-modal Contrastive Learning
- Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
- Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
- Transformers are Minimax Optimal Nonparametric In-Context Learners
- Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization
- Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods
- Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics
- Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
- DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
- Diffusion Models are Minimax Optimal Distribution Estimators
- Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
- Tight and fast generalization error bound of graph embedding in metric space
- Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction
- Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond
- Gradient-Based Feature Learning under Structured Data
- Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
- Dimension-free convergence rates for gradient Langevin dynamics in RKHS
- Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness
- Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization
- Understanding the Variance Collapse of SVGD in High Dimensions
- Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning
- High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
- Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
- Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime
- Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
- Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
- When does preconditioning help or hurt generalization?
- Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
- On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
- Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
- Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
- Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
- Differentiable Multiple Shooting Layers
- Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis
- Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
著者の全期間ページへの追加分（出版時所属は示していません）
- Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint
著者の全期間ページへの追加分（出版時所属は示していません）
- Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
著者の全期間ページへの追加分（出版時所属は示していません）
- Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
著者の全期間ページへの追加分（出版時所属は示していません）
- Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
著者の全期間ページへの追加分（出版時所属は示していません）
- Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
著者ページの大学: 東京大学
- Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
著者の全期間ページへの追加分（出版時所属は示していません）
- Approximation and non-parametric estimation of ResNet-type convolutional neural networks
著者ページの大学: 東京大学
- Functional Gradient Boosting based on Residual Network Perception
著者ページの大学: 東京大学
- Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
著者の全期間ページへの追加分（出版時所属は示していません）
- Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
著者の全期間ページへの追加分（出版時所属は示していません）
- Trimmed Density Ratio Estimation
著者の全期間ページへの追加分（出版時所属は示していません）
- Gaussian process nonparametric tensor estimator and its minimax optimality
著者の全期間ページへの追加分（出版時所属は示していません）
- Structure Learning of Partitioned Markov Networks
著者の全期間ページへの追加分（出版時所属は示していません）
- Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
著者の全期間ページへの追加分（出版時所属は示していません）
- Support Consistency of Direct Sparse-Change Learning in Markov Networks
著者の全期間ページへの追加分（出版時所属は示していません）
- Convergence rate of Bayesian tensor estimator and its minimax optimality
著者の全期間ページへの追加分（出版時所属は示していません）
- Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers
著者の全期間ページへの追加分（出版時所属は示していません）
- Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method
著者ページの大学: 東京大学
- Convex Tensor Decomposition via Structured Schatten Norm Regularization
著者の全期間ページへの追加分（出版時所属は示していません）
- A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems
著者の全期間ページへの追加分（出版時所属は示していません）
- PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model
著者ページの大学: 東京大学
- Density-Difference Estimation
著者の全期間ページへの追加分（出版時所属は示していません）
- Relative Density-Ratio Estimation for Robust Distribution Comparison
著者の全期間ページへの追加分（出版時所属は示していません）
- Statistical Performance of Convex Tensor Decomposition
著者の全期間ページへの追加分（出版時所属は示していません）
- Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning
著者ページの大学: 東京大学
- A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices.
著者の全期間ページへの追加分（出版時所属は示していません）