Taiji Suzuki

发表

Density Ratio Estimation in Machine Learning: Conclusions and Future Directions

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space pdf

Taiji Suzuki, Atsushi Nitanda, 2019, ArXiv.

Estimating Squared-Loss Mutual Information for Independent Component Analysis

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2009, ICA.

Generalized ridge estimator and model selection criteria in multivariate linear regression

Taiji Suzuki, Yuichi Mori, Taiji Suzuki, 2016, J. Multivar. Anal..

Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks pdf

Taiji Suzuki, Kenta Oono, Taiji Suzuki, 2019, ICML.

Regularization Strategies and Empirical Bayesian Learning for MKL pdf

Taiji Suzuki, Ryota Tomioka, 2010, ArXiv.

Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines

Taiji Suzuki, Atsushi Nitanda, Taiji Suzuki, 2017, AISTATS.

Support Consistency of Direct Sparse-Change Learning in Markov Networks

Masashi Sugiyama, Taiji Suzuki, Song Liu, 2015, AAAI.

Conditional Density Estimation via Least-Squares Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, AISTATS.

Gradient Descent in RKHS with Importance Labeling pdf

Taiji Suzuki, Tomoya Murata, Taiji Suzuki, 2020, AISTATS.

Computational complexity of kernel-based density-ratio estimation: a condition number analysis

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012, Machine Learning.

Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees

Taiji Suzuki, Atsushi Nitanda, Taiji Suzuki, 2020, AISTATS.

Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality pdf

Taiji Suzuki, Taiji Suzuki, 2018, ICLR.

Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum

Taiji Suzuki, Edgar Simo-Serra, Yosuke Shinya, 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network pdf

Taiji Suzuki, Taiji Suzuki, 2019, ICLR.

Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization

Taiji Suzuki, Atsushi Nitanda, Tomoya Murata, 2019, Knowledge and Information Systems.

A Density-ratio Framework for Statistical Data Processing

Takafumi Kanamori, Liwei Wang, Masashi Sugiyama, 2009, IPSJ Trans. Comput. Vis. Appl..

Cross-domain Recommendation via Deep Domain Adaptation

Taiji Suzuki, Nobuyuki Shimizu, Yukihiro Tagami, 2018, ECIR.

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

Masashi Sugiyama, Taiji Suzuki, John A. Quinn, 2013, Neural Computation.

Selective Inference for Latent Block Models pdf

Taiji Suzuki, Chihiro Watanabe, 2020, ArXiv.

A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems pdf

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki, 2012, COLT.

MATHEMATICAL ENGINEERING TECHNICAL REPORTS SpicyMKL

Taiji Suzuki, Ryota Tomioka, Taiji Suzuki, 2009 .

On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces

Taiji Suzuki, Satoshi Hayakawa, Taiji Suzuki, 2019, Neural Networks.

Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Taiji Suzuki, Kenta Oono, Taiji Suzuki, 2019, ICLR.

System identification and parameter estimation in mathematical medicine: examples demonstrated for prostate cancer

Kazuyuki Aihara, Taiji Suzuki, Kai Morino, 2016, Quantitative Biology.

Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning

Taiji Suzuki, Nobuyuki Shimizu, Yukihiro Tagami, 2016, NIPS.

Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method

Taiji Suzuki, Taiji Suzuki, 2013, ICML.

Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error

Taiji Suzuki, Hiroshi Abe, Tomoya Murata, 2018, IJCAI.

Functional Gradient Boosting based on Residual Network Perception

Taiji Suzuki, Atsushi Nitanda, Taiji Suzuki, 2018, ICML.

Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime pdf

Taiji Suzuki, Atsushi Nitanda, 2020, ArXiv.

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models pdf

Taiji Suzuki, Atsushi Nitanda, 2018, AISTATS.

Spectral-Pruning: Compressing deep neural network via spectral analysis pdf

Taiji Suzuki, Hiroshi Abe, Tomoya Murata, 2018, ArXiv.

$f$ -Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEEE Transactions on Information Theory.

MATHEMATICAL ENGINEERING TECHNICAL REPORTS Use of Primal-Dual Technique in the Network Algorithm for Two-Way Contingency Tables

Taiji Suzuki, Satoshi Aoki, Kazuo Murota, 2004 .

Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning

Taiji Suzuki, Taiji Suzuki, 2011, NIPS.

Stochastic Particle Gradient Descent for Infinite Ensembles pdf

Taiji Suzuki, Atsushi Nitanda, Taiji Suzuki, 2017, ArXiv.

A Curve Free Method based on Bayesian Optimization for Oncology Phase I Clinical Trials

Taiji Suzuki, Ami Takahashi, Taiji Suzuki, 2020 .

Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2010, Neural Computation.

On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective pdf

Taiji Suzuki, Kenta Oono, Taiji Suzuki, 2019, ArXiv.

Learning to estimate user interest utilizing the variational Bayes estimator

Kazuyuki Aihara, Taiji Suzuki, Hiroshi Tsujino, 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2012, AISTATS.

Least-squares two-sample test

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2011, Neural Networks.

Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics pdf

Taiji Suzuki, Taiji Suzuki, 2020, NeurIPS.

Density Ratio Estimation in Machine Learning: Preface

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Bayesian optimization design for dose‐finding based on toxicity and efficacy outcomes in phase I/II clinical trials

Taiji Suzuki, Ami Takahashi, Taiji Suzuki, 2020, Pharmaceutical statistics.

Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation

Masashi Sugiyama, Taiji Suzuki, Ryota Tomioka, 2009, J. Mach. Learn. Res..

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis pdf

Taiji Suzuki, Denny Wu, Atsushi Nitanda, 2020, ArXiv.

PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model

Taiji Suzuki, Taiji Suzuki, 2012, COLT.

Learning sparse structural changes in high-dimensional Markov networks

Kenji Fukumizu, Taiji Suzuki, Song Liu, 2017, Behaviormetrika.

Conjugate relation between loss functions and uncertainty sets in classification problems

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki, 2013, J. Mach. Learn. Res..

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks pdf

Taiji Suzuki, Kenta Oono, Taiji Suzuki, 2020, NeurIPS.

Convergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity pdf

Taiji Suzuki, 2014, ArXiv.

Stochastic dual averaging methods using variance reduction techniques for regularized empirical risk minimization problems pdf

Taiji Suzuki, Tomoya Murata, 2016, ArXiv.

A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices

Masashi Sugiyama, Hisashi Kashima, Taiji Suzuki, 2010, ICML.

Short-term local weather forecast using dense weather station by deep neural network

Taiji Suzuki, Hitoshi Hattori, Kazuo Yonekura, 2018, 2018 IEEE International Conference on Big Data (Big Data).

Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features pdf

Taiji Suzuki, Atsushi Nitanda, Shingo Yashima, 2019, ArXiv.

Applications of Density Ratios in Machine Learning

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Mutual Information Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization

Taiji Suzuki, Tomoya Murata, 2017, NIPS.

Understanding Generalization in Deep Learning via Tensor Methods

Taiji Suzuki, Furong Huang, Yanchao Sun, 2020, AISTATS.

Goodness-of-fit Test for Latent Block Models pdf

Taiji Suzuki, Chihiro Watanabe, Taiji Suzuki, 2019, Comput. Stat. Data Anal..

Least-Squares Independent Component Analysis

Masashi Sugiyama, Taiji Suzuki, Masashi Sugiyama, 2011, Neural Computation.

Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks

Taiji Suzuki, Atsushi Yaguchi, Akiyuki Tanizawa, 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD pdf

Taiji Suzuki, Mitsuhiro Kimura, Takeshi Toda, 2019, ArXiv.

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search

Motoaki Kawanabe, Takafumi Kanamori, Masashi Sugiyama, 2011, Neural Networks.

Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation

Taiji Suzuki, Tomoya Murata, Taiji Suzuki, 2018, NeurIPS.

Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error pdf

Taiji Suzuki, Yusuke Hayashi, 2020, ArXiv.

A Hybrid Systems Approach to Hormonal Therapy of Prostate Cancer and its Nonlinear Dynamics

Gouhei Tanaka, Kazuyuki Aihara, Taiji Suzuki, 2007 .

A Consistent Method for Graph Based Anomaly Localization

Taiji Suzuki, Satoshi Hara, Tetsuro Morimura, 2015, AISTATS.

Theoretical Analysis of Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Structure Learning of Partitioned Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2016, ICML.

Statistical analysis of kernel-based least-squares density-ratio estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012, Machine Learning.

Dimension-free convergence rates for gradient Langevin dynamics in RKHS pdf

Taiji Suzuki, Mathurin Massias, Kanji Sato, 2020, ArXiv.

On Learning Sparse Changes of Markov Networks

Masashi Sugiyama, Kenji Fukumizu, Taiji Suzuki, 2014 .

Scalable Deep Neural Networks via Low-Rank Matrix Factorization pdf

Taiji Suzuki, Atsushi Yaguchi, Akiyuki Tanizawa, 2019, ArXiv.

Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint

Taiji Suzuki, Jimmy Ba, Murat A. Erdogdu, 2020, ICLR.

Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning

Masashi Sugiyama, Taiji Suzuki, Ryota Tomioka, 2009 .

Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems

Taiji Suzuki, Atsushi Nitanda, Geoffrey Chinot, 2019, ArXiv.

Mutual information estimation reveals global associations between stimuli and biological processes

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2009, BMC Bioinformatics.

Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2013, J. Comput. Sci. Eng..

Gaussian process nonparametric tensor estimator and its minimax optimality

Taiji Suzuki, Nobuyuki Shimizu, Yukihiro Tagami, 2016, ICML.

Overview of Statistical Learning Theory(Survey, Activity Group "Machine Learning")

Taiji Suzuki, Taiji Suzuki, 2013 .

Improvement of multiple kernel learning using adaptively weighted regularization

Taiji Suzuki, Taiji Suzuki, 2013, JSIAM Lett..

Relative Density-Ratio Estimation for Robust Distribution Comparison

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2011, Neural Computation.

Statistical Performance of Convex Tensor Decomposition

Hisashi Kashima, Taiji Suzuki, Ryota Tomioka, 2011, NIPS.

Convergence rate of Bayesian tensor estimator and its minimax optimality

Taiji Suzuki, Taiji Suzuki, 2015, ICML.

Density-Difference Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012, Neural Computation.

Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2008, FSDM.

Independently Interpretable Lasso for Generalized Linear Models

Taiji Suzuki, Hironori Fujisawa, Masaaki Takada, 2020, Neural Computation.

Least-Squares Independence Test

Masashi Sugiyama, Taiji Suzuki, 2011, IEICE Trans. Inf. Syst..

Augmented Lagrangian Methods for Learning, Selecting, and Combining Features

Masashi Sugiyama, Taiji Suzuki, Ryota Tomioka, 2011 .

Density Ratio Estimation : A Comprehensive Review (Statistical Experiment and Its Related Topics)

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010 .

Domain Adaptation Regularization for Spectral Pruning pdf

Taiji Suzuki, Yosuke Shinya, Laurent Dillard, 2020, BMVC.

Mutual information approximation via maximum likelihood estimation of density ratio

Masashi Sugiyama, Taiji Suzuki, Toshiyuki Tanaka, 2009, 2009 IEEE International Symposium on Information Theory.

Neural Architecture Search Using Stable Rank of Convolutional Layers pdf

Taiji Suzuki, Kuniaki Uto, Koichi Shinoda, 2020, ArXiv.

Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables

Taiji Suzuki, Hironori Fujisawa, Masaaki Takada, 2017, AISTATS.

When Does Preconditioning Help or Hurt Generalization? pdf

Taiji Suzuki, Roger B. Grosse, Ji Xu, 2020, ICLR.

Fast learning rate of deep learning via a kernel perspective pdf

Taiji Suzuki, Taiji Suzuki, 2017, ArXiv.

Least-Squares Conditional Density Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEICE Trans. Inf. Syst..

Asian Conference on Machine Learning: Preface

Taiji Suzuki, Wee Sun Lee, Taiji Suzuki, 2019, ACML.

Decomposable-Net: Scalable Low-Rank Compression for Neural Networks

Taiji Suzuki, Atsushi Yaguchi, Akiyuki Tanizawa, 2019, IJCAI.

A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model

Kenji Fukumizu, Taiji Suzuki, Shaogao Lv, 2020, Comput. Stat. Data Anal..

Density Ratio Estimation in Machine Learning

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2012 .

Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers

Taiji Suzuki, Taiji Suzuki, 2014, ICML.

Trimmed Density Ratio Estimation

Akiko Takeda, Kenji Fukumizu, Taiji Suzuki, 2017, NIPS.

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors

Taiji Suzuki, Atsushi Nitanda, 2019, AISTATS.

SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels

Taiji Suzuki, Ryota Tomioka, Ryota Tomioka, 2011, Machine Learning.

Accelerated Sparsified SGD with Error Feedback pdf

Taiji Suzuki, Tomoya Murata, Taiji Suzuki, 2019, ArXiv.

Convex Tensor Decomposition via Structured Schatten Norm Regularization

Taiji Suzuki, Ryota Tomioka, Ryota Tomioka, 2013, NIPS.

Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space pdf

Taiji Suzuki, Kazuma Tsuji, 2020, ArXiv.

Direct Density Ratio Estimation with Dimensionality Reduction

Motoaki Kawanabe, Takafumi Kanamori, Masashi Sugiyama, 2010, SDM.

Fast generalization error bound of deep learning from a kernel perspective

Taiji Suzuki, Taiji Suzuki, 2018, AISTATS.