vix.ing · top · new · best · stats · spec

Taiji Suzuki

  1. Diffusion Models are Minimax Optimal Distribution Estimators
    2023/03/03 by Kazusato Oko, Shunta Akiyama, Oko, Kazusato +3 · 23 citations
    Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Radiomics and Machine Learning in Medical Imaging #Statistical Methods and Inference
  2. Relative Density-Ratio Estimation for Robust Distribution Comparison
    2011/06/23 by Makoto Yamada, Taiji Suzuki, Yamada, Makoto +7 · 8 citations
    Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Methodology (stat.ME) #Statistics Theory (math.ST)
  3. Convex Analysis of the Mean Field Langevin Dynamics
    2022/01/25 by Atsushi Nitanda, Denny Wu, Nitanda, Atsushi +3 · 7 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Stochastic Gradient Optimization Techniques
  4. Mechanistic Design and Scaling of Hybrid Architectures
    2024/03/26 by Michael Poli, Armin W. Thomas, Poli, Michael +21 · 11 citations
    Engineering · #Architecture and Computational Design
  5. Pretrained transformer efficiently learns low-dimensional target functions in-context
    2024/11/04 by Kazusato Oko, Oko, Kazusato, Yujin Song +5 · 14 citations
    Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  6. When Does Preconditioning Help or Hurt Generalization?
    2020/06/18 by Шун-ичи Амари, Amari, Shun-ichi, Jimmy Ba +13 · 5 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques
  7. On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
    2021/06/11 by Shunta Akiyama, Akiyama, Shunta, Taiji Suzuki +1 · 3 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  8. Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
    2024/02/02 by Juno Kim, Taiji Suzuki, Kim, Juno +1 · 7 citations
    Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  9. Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
    2023/05/30 by Shokichi Takakura, Takakura, Shokichi, Taiji Suzuki +1 · 4 citations
    Computer Science · Medicine · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging
  10. Flow matching achieves almost minimax optimal convergence
    2024/05/31 by Kenji Fukumizu, Taiji Suzuki, Fukumizu, Kenji +7 · 5 citations
    Engineering · Computer Science · #Advanced Control Systems Optimization #Reinforcement Learning in Robotics
  11. Gradient-Based Feature Learning under Structured Data
    2023/09/07 by Alireza Mousavi-Hosseini, Denny Wu, Mousavi-Hosseini, Alireza +5 · 5 citations
    Computer Science · Environmental Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  12. State-Free Inference of State-Space Models: The Transfer Function Approach
    2024/05/10 by Rom N. Parnichkun, Parnichkun, Rom N., Stefano Massaroli +23 · 5 citations
    Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering
  13. Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
    2019/05/23 by Atsushi Nitanda, Geoffrey Chinot, Nitanda, Atsushi +3 · 3 citations
    Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
  14. Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods
    2022/05/30 by Shunta Akiyama, Akiyama, Shunta, Taiji Suzuki +1 · 3 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Stochastic Gradient Optimization Techniques
  15. Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
    2025/02/02 by Juno Kim, Denny Wu, Kim, Juno +5 · 7 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  16. How do Transformers perform In-Context Autoregressive Learning?
    2024/02/08 by Michael E. Sander, Raja Giryes, Sander, Michael E. +7 · 4 citations
    Computer Science · #Topic Modeling #Natural Language Processing Techniques #Speech Recognition and Synthesis
  17. Convex Tensor Decomposition via Structured Schatten Norm Regularization
    2013/03/26 by Ryota Tomioka, Tomioka, Ryota, Taiji Suzuki +1 · 2 citations
    Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications
  18. Understanding Generalization in Deep Learning via Tensor Methods
    2020/01/14 by Jingling Li, Li, Jingling, Yanchao Sun +7 · 3 citations
    Computer Science · Mathematics · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Computer and information sciences #I.2.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Tensor decomposition and applications
  19. Density-Difference Estimation
    2012/06/30 by Masashi Sugiyama, Sugiyama, Masashi, Takafumi Kanamori +9 · 1 citation
    Computer Science · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Target Tracking and Data Fusion in Sensor Networks
  20. Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
    2024/06/17 by Kazusato Oko, Yujin Song, Oko, Kazusato +5 · 3 citations
    Computer Science · #Neural Networks and Applications
  21. Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
    2018/08/26 by Taiji Suzuki, Hiroshi Abe, Suzuki, Taiji +15 · 2 citations
    Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference #Neural Networks and Applications
  22. Learning Green's Function Efficiently Using Low-Rank Approximations
    2023/08/01 by Kishan Wimalawarne, Taiji Suzuki, Wimalawarne, Kishan +3 · 2 citations
    Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Matrix Theory and Algorithms #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
  23. Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
    2017/11/06 by Masaaki Takada, Takada, Masaaki, Taiji Suzuki +3 · 1 citation
    Computer Science · Engineering · Mathematics · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
  24. Cross-domain Recommendation via Deep Domain Adaptation
    2018/03/08 by Heishiro Kanagawa, Hayato Kobayashi, Kanagawa, Heishiro +7 · 1 citation
    Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #Topic Modeling
  25. Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
    2024/03/22 by Shokichi Takakura, Taiji Suzuki, Takakura, Shokichi +1 · 2 citations
    Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  26. Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction
    2023/06/12 by Taiji Suzuki, Denny Wu, Suzuki, Taiji +3 · 2 citations
    Computer Science · Physics and Astronomy · Mathematics · #Stochastic Gradient Optimization Techniques #Model Reduction and Neural Networks #Markov Chains and Monte Carlo Methods
  27. Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
    2022/03/30 by Yuri Kinoshita, Taiji Suzuki, Kinoshita, Yuri +1 · 1 citation
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  28. DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
    2023/02/08 by Tomoya Murata, Taiji Suzuki, Murata, Tomoya +1 · 1 citation
    Computer Science · Medicine · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Pharmacological Effects and Toxicity Studies #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques
  29. Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
    2023/12/02 by Juno Kim, Kim, Juno, Kakei Yamamoto +7 · 1 citation
    Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
  30. State Space Models are Comparable to Transformers in Estimating Functions with Dynamic Smoothness
    2024/05/29 by Naoki Nishikawa, Taiji Suzuki, Nishikawa, Naoki +1 · 1 citation
    Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  31. High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
    2024/06/05 by Yihang Chen, Fanghui Liu, Chen, Yihang +5 · 1 citation
    Mathematics · Engineering · Computer Science · #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference
  32. On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
    2024/10/07 by Bingrui Li, Li, Bingrui, Wei Huang +9 · 1 citation
    Engineering · #Advanced Numerical Analysis Techniques #Advanced Research in Systems and Signal Processing #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optical Polarization and Ellipsometry
  33. Direct Distributional Optimization for Provable Alignment of Diffusion Models
    2025/02/05 by Ryotaro Kawata, Kawata, Ryotaro, Kazusato Oko +5 · 1 citation
    Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topology Optimization in Engineering
  34. When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars
    2025/04/24 by Higuchi, Rei, Ryotaro Kawata, Kawata, Ryotaro +16 · 1 citation
    Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling
  35. Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric
    2024/04/30 by Toshimitsu Uesaka, Uesaka, Toshimitsu, Taiji Suzuki +9 · 1 citation
    Arts and Humanities · Psychology · #EFL/ESL Teaching and Learning #FOS: Computer and information sciences #Innovative Teaching and Learning Methods #Machine Learning (cs.LG)
  36. Zero-Flow Two-Sample Tests
    2026/07/23 by Yakun Wang, Leyang Wang, Song Liu +1
    #cs.LG #stat.ML