Suzuki, Taiji

142 publications

AISTATS 2025 Clustered Invariant Risk Minimization Tomoya Murata, Atsushi Nitanda, Taiji Suzuki

NeurIPS 2025 Degrees of Freedom for Linear Attention: Distilling SoftMax Attention with Optimal Feature Efficiency Naoki Nishikawa, Rei Higuchi, Taiji Suzuki

ICML 2025 Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models Rei Higuchi, Taiji Suzuki

ICLR 2025 Direct Distributional Optimization for Provable Alignment of Diffusion Models Ryotaro Kawata, Kazusato Oko, Atsushi Nitanda, Taiji Suzuki

CPAL 2025 Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks Wei Huang, Wuyang Chen, Zhiqiang Xu, Zhangyang Wang, Taiji Suzuki

ICLR 2025 Flow Matching Achieves Almost Minimax Optimal Convergence Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama

NeurIPS 2025 From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers Ryotaro Kawata, Yujin Song, Alberto Bietti, Naoki Nishikawa, Taiji Suzuki, Samuel Vaiter, Denny Wu

NeurIPS 2025 Generalization Bound of Gradient Flow Through Training Trajectory and Data-Dependent Kernel Yilan Chen, Zhichao Wang, Wei Huang, Andi Han, Taiji Suzuki, Arya Mazumdar

NeurIPS 2025 Hessian-Guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points Naoya Yamamoto, Juno Kim, Taiji Suzuki

NeurIPS 2025 How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? Wei Huang, Andi Han, Yujin Song, Yilan Chen, Denny Wu, Difan Zou, Taiji Suzuki

MLJ 2025 Learning Green's Function Efficiently Using Low-Rank Approximations Kishan Wimalawarne, Taiji Suzuki, Sophie Langer

ICML 2025 Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation Juno Kim, Denny Wu, Jason D. Lee, Taiji Suzuki

ICML 2025 Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning Ryotaro Kawata, Kohsei Matsutani, Yuri Kinoshita, Naoki Nishikawa, Taiji Suzuki

ICML 2025 Nonlinear Transformers Can Perform Inference-Time Feature Learning Naoki Nishikawa, Yujin Song, Kazusato Oko, Denny Wu, Taiji Suzuki

ICLR 2025 On the Optimization and Generalization of Two-Layer Transformers with Sign Gradient Descent Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen

ICML 2025 On the Role of Label Noise in the Feature Learning Process Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki

ICLR 2025 Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li

ICML 2025 Propagation of Chaos for Mean-Field Langevin Dynamics and Its Application to Model Ensemble Atsushi Nitanda, Anzelle Lee, Damian Tan Xing Kai, Mizuki Sakaguchi, Taiji Suzuki

ICML 2025 Provable In-Context Vector Arithmetic via Retrieving Task Concepts Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki

ICML 2025 Quantifying Memory Utilization with Effective State-Size Rom Parnichkun, Neehal Tumma, Armin W Thomas, Alessandro Moro, Qi An, Taiji Suzuki, Atsushi Yamashita, Michael Poli, Stefano Massaroli

AISTATS 2025 Quantifying the Optimization and Generalization Advantages of Graph Neural Networks over Multilayer Perceptrons Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki

NeurIPS 2025 State Size Independent Statistical Error Bound for Discrete Diffusion Models Shintaro Wakasugi, Taiji Suzuki

ICLR 2025 State Space Models Are Provably Comparable to Transformers in Dynamic Token Selection Naoki Nishikawa, Taiji Suzuki

NeurIPS 2025 Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie

ICLR 2025 Transformers Provably Solve Parity Efficiently with Chain of Thought Juno Kim, Taiji Suzuki

ICLR 2025 Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida, Chieh-Hsin Lai, Naoki Murata, Yuki Mitsufuji

ECML-PKDD 2024 Dimensionality-Induced Information Loss of Outliers in Deep Neural Networks Kazuki Uematsu, Kosuke Haruki, Taiji Suzuki, Mitsuhiro Kimura, Takahiro Takimoto, Hideyuki Nakagawa

ICML 2024 High-Dimensional Kernel Methods Under Covariate Shift: Data-Dependent Implicit Regularization Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher

ICML 2024 How Do Transformers Perform In-Context Autoregressive Learning ? Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré

ICLR 2024 Improved Statistical and Computational Complexity of the Mean-Field Langevin Dynamics Under Structured Data Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu

ICLR 2024 Koopman-Based Generalization Bound: New Aspect for Full-Rank Weights Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki

COLT 2024 Learning Sum of Diverse Features: Computational Hardness and Efficient Gradient-Based Training for Ridge Combinations Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu

ICML 2024 Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality Beyond Lazy Learning Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki

ICML 2024 Mean-Field Analysis on Two-Layer Neural Networks from a Kernel Perspective Shokichi Takakura, Taiji Suzuki

ICML 2024 Mechanistic Design and Scaling of Hybrid Architectures Michael Poli, Armin W Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Re, Ce Zhang, Stefano Massaroli

ICLR 2024 Minimax Optimality of Convolutional Neural Networks for Infinite Dimensional Input-Output Problems and Separation from Kernel Methods Yuto Nishimura, Taiji Suzuki

ICMLW 2024 Neural Network Learns Low-Dimensional Polynomials with SGD near the Information-Theoretic Limit Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu

NeurIPS 2024 Neural Network Learns Low-Dimensional Polynomials with SGD near the Information-Theoretic Limit Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu

NeurIPS 2024 On the Comparison Between Multi-Modal and Single-Modal Contrastive Learning Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki

ICLR 2024 Optimal Criterion for Feature Learning of Two-Layer Linear Neural Network in High Dimensional Interpolation Regime Keita Suzuki, Taiji Suzuki

NeurIPSW 2024 Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li

NeurIPS 2024 Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu

ICML 2024 Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong

NeurIPS 2024 Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong

ICML 2024 SILVER: Single-Loop Variance Reduction and Application to Federated Learning Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki

ICMLW 2024 State Space Models Are Comparable to Transformers in Estimating Functions with Dynamic Smoothness Naoki Nishikawa, Taiji Suzuki

ICML 2024 State-Free Inference of State-Space Models: The *Transfer Function* Approach Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T.H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Re, Hajime Asama, Stefano Ermon, Taiji Suzuki, Michael Poli, Atsushi Yamashita

ICLR 2024 Symmetric Mean-Field Langevin Dynamics for Distributional Minimax Problems Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki

ICMLW 2024 Transformer Efficiently Learns Low-Dimensional Target Functions In-Context Yujin Song, Denny Wu, Kazusato Oko, Taiji Suzuki

NeurIPS 2024 Transformers Are Minimax Optimal Nonparametric In-Context Learners Juno Kim, Tai Nakamaki, Taiji Suzuki

ICMLW 2024 Transformers Are Minimax Optimal Nonparametric In-Context Learners Juno Kim, Tai Nakamaki, Taiji Suzuki

ICMLW 2024 Transformers Are Minimax Optimal Nonparametric In-Context Learners Juno Kim, Tai Nakamaki, Taiji Suzuki

ICLRW 2024 Transformers Learn Nonlinear Features in Context Juno Kim, Taiji Suzuki

ICML 2024 Transformers Learn Nonlinear Features in Context: Nonconvex Mean-Field Dynamics on the Attention Landscape Juno Kim, Taiji Suzuki

NeurIPSW 2024 Transformers Provably Solve Parity Efficiently with Chain of Thought Juno Kim, Taiji Suzuki

ICLR 2024 Understanding Convergence and Generalization in Federated Learning Through Feature Learning Theory Wei Huang, Ye Shi, Zhongyi Cai, Taiji Suzuki

NeurIPS 2024 Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie

ICML 2023 Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input Shokichi Takakura, Taiji Suzuki

NeurIPS 2023 Convergence of Mean-Field Langevin Dynamics: Time-Space Discretization, Stochastic Gradient, and Variance Reduction Taiji Suzuki, Denny Wu, Atsushi Nitanda

ICML 2023 DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning Tomoya Murata, Taiji Suzuki

ICML 2023 Diffusion Models Are Minimax Optimal Distribution Estimators Kazusato Oko, Shunta Akiyama, Taiji Suzuki

ICLRW 2023 Diffusion Models Are Minimax Optimal Distribution Estimators Kazusato Oko, Shunta Akiyama, Taiji Suzuki

ICLR 2023 Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and Its Superiority to Kernel Methods Shunta Akiyama, Taiji Suzuki

NeurIPS 2023 Feature Learning via Mean-Field Langevin Dynamics: Classifying Sparse Parities and Beyond Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda

NeurIPS 2023 Gradient-Based Feature Learning Under Structured Data Alireza Mousavi-Hosseini, Denny Wu, Taiji Suzuki, Murat A Erdogdu

NeurIPSW 2023 Graph Neural Networks Benefit from Structural Information Provably: A Feature Learning Perspective Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki

NeurIPSW 2023 How Structured Data Guides Feature Learning: A Case Study of the Parity Problem Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu

ICMLW 2023 Learning Green's Function Efficiently Using Low-Rank Approximations Kishan Wimalawarne, Taiji Suzuki, Sophie Langer

NeurIPS 2023 Learning in the Presence of Low-Dimensional Structure: A Spiked Random Matrix Perspective Jimmy Ba, Murat A Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu

ICML 2023 Primal and Dual Analysis of Entropic Fictitious Play for Finite-Sum Problems Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki

NeurIPSW 2023 Symmetric Mean-Field Langevin Dynamics for Distributional Minimax Problems Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki

ICML 2023 Tight and Fast Generalization Error Bound of Graph Embedding in Metric Space Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi

ICLR 2023 Uniform-in-Time Propagation of Chaos for the Mean-Field Gradient Langevin Dynamics Taiji Suzuki, Atsushi Nitanda, Denny Wu

AISTATS 2022 Convex Analysis of the Mean Field Langevin Dynamics Atsushi Nitanda, Denny Wu, Taiji Suzuki

ECML-PKDD 2022 A Scaling Law for Syn2real Transfer: How Much Is Your Pre-Training Effective? Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi

COLT 2022 Dimension-Free Convergence Rates for Gradient Langevin Dynamics in RKHS Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki

NeurIPS 2022 Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning Tomoya Murata, Taiji Suzuki

NeurIPS 2022 High-Dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation Jimmy Ba, Murat A Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang

NeurIPS 2022 Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and Its Application to Optimization Yuri Kinoshita, Taiji Suzuki

ACML 2022 Layer-Wise Adaptive Graph Convolution Networks Using Generalized Pagerank Kishan Wimalawarne, Taiji Suzuki

ICLR 2022 Learnability of Convolutional Neural Networks for Infinite Dimensional Input via Mixed and Anisotropic Smoothness Sho Okumoto, Taiji Suzuki

ICLR 2022 Particle Stochastic Dual Coordinate Ascent: Exponential Convergent Algorithm for Mean Field Neural Network Optimization Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu

NeurIPSW 2022 Reducing Communication in Nonconvex Federated Learning with a Novel Single-Loop Variance Reduction Method Kazusato Oko, Shunta Akiyama, Tomoya Murata, Taiji Suzuki

NeurIPS 2022 Two-Layer Neural Network on Infinite Dimensional Data: Global Optimization Guarantee in the Mean-Field Regime Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu

ICLR 2022 Understanding the Variance Collapse of SVGD in High Dimensions Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang

AISTATS 2021 Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features Shingo Yashima, Atsushi Nitanda, Taiji Suzuki

AISTATS 2021 Gradient Descent in RKHS with Importance Labeling Tomoya Murata, Taiji Suzuki

ICLR 2021 Benefit of Deep Learning with Non-Convex Noisy Gradient Descent: Provable Excess Risk Bound and Superiority to Kernel Methods Taiji Suzuki, Shunta Akiyama

ICML 2021 Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning Tomoya Murata, Taiji Suzuki

IJCAI 2021 Decomposable-Net: Scalable Low-Rank Compression for Neural Networks Atsushi Yaguchi, Taiji Suzuki, Shuhei Nitta, Yukinobu Sakata, Akiyuki Tanizawa

NeurIPS 2021 Deep Learning Is Adaptive to Intrinsic Dimensionality of Model Smoothness in Anisotropic Besov Space Taiji Suzuki, Atsushi Nitanda

NeurIPS 2021 Differentiable Multiple Shooting Layers Stefano Massaroli, Michael Poli, Sho Sonoda, Taiji Suzuki, Jinkyoo Park, Atsushi Yamashita, Hajime Asama

ICML 2021 On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting Shunta Akiyama, Taiji Suzuki

ICLR 2021 Optimal Rates for Averaged Stochastic Gradient Descent Under Neural Tangent Kernel Regime Atsushi Nitanda, Taiji Suzuki

NeurIPS 2021 Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis Atsushi Nitanda, Denny Wu, Taiji Suzuki

ICML 2021 Quantitative Understanding of VAE as a Non-Linearly Scaled Isometric Embedding Akira Nakagawa, Keizo Kato, Taiji Suzuki

ICLR 2021 When Does Preconditioning Help or Hurt Generalization? Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu

ICLR 2020 Compression Based Bound for Non-Compressed Network: Unified Generalization Error Analysis of Large Compressible Deep Neural Network Taiji Suzuki, Hiroshi Abe, Tomoaki Nishimura

AISTATS 2020 Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees Atsushi Nitanda, Taiji Suzuki

NeurIPS 2020 Generalization Bound of Globally Optimal Non-Convex Neural Network Training: Transportation mAP Estimation by Infinite Dimensional Langevin Dynamics Taiji Suzuki

ICLR 2020 Generalization of Two-Layer Neural Networks: An Asymptotic Viewpoint Jimmy Ba, Murat Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang

ICLR 2020 Graph Neural Networks Exponentially Lose Expressive Power for Node Classification Kenta Oono, Taiji Suzuki

NeurIPS 2020 Optimization and Generalization Analysis of Transduction Through Gradient Boosting and Application to Multi-Scale Graph Neural Networks Kenta Oono, Taiji Suzuki

IJCAI 2020 Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and Its Generalization Error Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura

AISTATS 2020 Understanding Generalization in Deep Learning via Tensor Methods Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang

ICLR 2019 Adaptivity of Deep ReLU Network for Learning in Besov and Mixed Smooth Besov Spaces: Optimal Rate and Curse of Dimensionality Taiji Suzuki

ICML 2019 Approximation and Non-Parametric Estimation of ResNet-Type Convolutional Neural Networks Kenta Oono, Taiji Suzuki

ACML 2019 Asian Conference on Machine Learning: Preface Wee Sun Lee, Taiji Suzuki

AISTATS 2019 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors Atsushi Nitanda, Taiji Suzuki

ICCVW 2019 Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum Yosuke Shinya, Edgar Simo-Serra, Taiji Suzuki

AISTATS 2018 Fast Generalization Error Bound of Deep Learning from a Kernel Perspective Taiji Suzuki

ICML 2018 Functional Gradient Boosting Based on Residual Network Perception Atsushi Nitanda, Taiji Suzuki

AISTATS 2018 Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models Atsushi Nitanda, Taiji Suzuki

AISTATS 2018 Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables Masaaki Takada, Taiji Suzuki, Hironori Fujisawa

NeurIPS 2018 Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation Tomoya Murata, Taiji Suzuki

NeurIPS 2017 Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata, Taiji Suzuki

AISTATS 2017 Stochastic Difference of Convex Algorithm and Its Application to Training Deep Boltzmann Machines Atsushi Nitanda, Taiji Suzuki

NeurIPS 2017 Trimmed Density Ratio Estimation Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu

ICML 2016 Gaussian Process Nonparametric Tensor Estimator and Its Minimax Optimality Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami

NeurIPS 2016 Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami

ICML 2016 Structure Learning of Partitioned Markov Networks Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu

AISTATS 2015 A Consistent Method for Graph Based Anomaly Localization Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa, Taiji Suzuki

ICML 2015 Convergence Rate of Bayesian Tensor Estimator and Its Minimax Optimality Taiji Suzuki

AAAI 2015 Support Consistency of Direct Sparse-Change Learning in Markov Networks Song Liu, Taiji Suzuki, Masashi Sugiyama

ICML 2014 Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers Taiji Suzuki

MLJ 2013 Computational Complexity of Kernel-Based Density-Ratio Estimation: A Condition Number Analysis Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama

JMLR 2013 Conjugate Relation Between Loss Functions and Uncertainty Sets in Classification Problems Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

NeurIPS 2013 Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki

ICML 2013 Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method Taiji Suzuki

COLT 2012 A Conjugate Property Between Loss Functions and Uncertainty Sets in Classification Problems Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

NeurIPS 2012 Density-Difference Estimation Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus D. Plessis, Song Liu, Ichiro Takeuchi

AISTATS 2012 Fast Learning Rate of Multiple Kernel Learning: Trade-Off Between Sparsity and Smoothness Taiji Suzuki, Masashi Sugiyama

COLT 2012 PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model Taiji Suzuki

MLJ 2012 Statistical Analysis of Kernel-Based Least-Squares Density-Ratio Estimation Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama

NeurIPS 2011 Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama

MLJ 2011 SpicyMKL: A Fast Algorithm for Multiple Kernel Learning with Thousands of Kernels Taiji Suzuki, Ryota Tomioka

NeurIPS 2011 Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima

JMLR 2011 Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama

NeurIPS 2011 Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki

ICML 2010 A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima

AISTATS 2010 Conditional Density Estimation via Least-Squares Density Ratio Estimation Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara

AISTATS 2010 Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation Taiji Suzuki, Masashi Sugiyama