← Authors

Masashi Sugiyama

224 papers · 2002–2026 · 17 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (55) 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (23) 🌍 Conference Polyglot (17) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (8) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (7) 🏠 Conference Loyalist (60) πŸ‘‘ Domain Dominant (6) πŸ† Keyword Champion (4) πŸ”¬ Deep Specialist (16) 🀝 Dynamic Duo (87) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (256) ❓ The Questioner (8) ⚑ Prolific Year (9) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (20) πŸš€ Conference Pioneer πŸ’Ž Century Club (223)

Conferences

NIPS (60) ICML (58) AISTATS (26) ICLR (20) ACML (16) JMLR (16) AAAI (9) IJCAI (4) ICCV (3) EMNLP (2) ECCV (2) CVPR (2) WACV (2) EACL (1) IJCNLP (1) COLT (1) UAI (1)

Top co-authors

Gang Niu (88) Bo Han (33) Tongliang Liu (22) Issei Sato (21) Jingfeng ZHANG (14) Shinichi Nakajima (12) Junya Honda (11) Nontawat Charoenphakdee (11) Lei Feng (10) Nan Lu (9)

Keywords

weakly supervised learning (19) variational inference (13) semi-supervised learning (13) label noise (13) domain adaptation (13) binary classification (11) bayesian inference (10) importance weighting (9) adversarial training (9) adversarial robustness (9) noisy label (9) distribution shift (8) density ratio estimation (8) online learning (7) kernel methods (7) dimensionality reduction (7) multi-armed bandit (7) multi-class classification (6) variational bayesian (6) representation learning (6)

Papers

Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer AAAI 2026
Label Distribution Learning with Biased Annotations Assisted by Multi-Label Learning IJCAI 2025
Multi-Player Approaches for Dueling Bandits AISTATS 2025
Domain Adaptation and Entanglement: an Optimal Transport Perspective AISTATS 2025
Learning View-invariant World Models for Visual Robotic Manipulation ICLR 2025
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods ICLR 2025
Towards Out-of-Modal Generalization without Instance-level Modal Correspondence ICLR 2025
Realistic Evaluation of Deep Partial-Label Learning Algorithms ICLR 2025
Sharpness-Aware Black-Box Optimization ICLR 2025
Adaptive Localization of Knowledge Negation for Continual LLM Unlearning ICML 2025
Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models ICML 2025
Action-Agnostic Point-Level Supervision for Temporal Action Detection AAAI 2025
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability ICML 2025
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation ICCV 2025
What Makes Partial-Label Learning Algorithms Effective? NIPS 2024
Slight Corruption in Pre-training Data Makes Better Diffusion Models NIPS 2024
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit AAAI 2024
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models AAAI 2024
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought ICML 2024
Balancing Similarity and Complementarity for Federated Learning ICML 2024
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training ICML 2024
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical ICML 2024
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation ICML 2024
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization ICML 2024
A General Framework for Learning from Weak Supervision ICML 2024
Accurate Forgetting for Heterogeneous Federated Continual Learning ICLR 2024
Robust Similarity Learning with Difference Alignment Regularization ICLR 2024
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks ICLR 2024
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification EMNLP 2024
Direct Distillation between Different Domains ECCV 2024
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning ECCV 2024
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit AISTATS 2024
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates AISTATS 2024
Appearance-Based Curriculum for Semi-Supervised Learning With Multi-Angle Unlabeled Data WACV 2024
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations NIPS 2024
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics NIPS 2024
Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment NIPS 2024
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost NIPS 2023
Imitation Learning from Vague Feedback NIPS 2023
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits ICML 2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection NIPS 2023
Universal Approximation Property of Invertible Neural Networks JMLR 2023
A Category-theoretical Meta-analysis of Definitions of Disentanglement ICML 2023
Thompson Exploration with Best Challenger Rule in Best Arm Identification ACML 2023
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective NIPS 2023
Binary Classification with Confidence Difference NIPS 2023
Distributional Pareto-Optimal Multi-Objective Reinforcement Learning NIPS 2023
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization NIPS 2023
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation NIPS 2023
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification ICLR 2023
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning ICLR 2023
Multi-Label Knowledge Distillation ICCV 2023
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images ICCV 2023
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems NIPS 2023
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning NIPS 2023
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation NIPS 2023
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation ICML 2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks ICML 2023
Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation CVPR 2022
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses NIPS 2022
Learning Contrastive Embedding in Low-Dimensional Space NIPS 2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks NIPS 2022
Adapting to Online Label Shift with Provable Guarantees NIPS 2022
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness NIPS 2022
Robust computation of optimal transport by $Ξ²$-potential regularization ACML 2022
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization ACML 2022
Pairwise Supervision Can Provably Elicit a Decision Boundary AISTATS 2022
Predictive variational Bayesian inference as risk-seeking optimization AISTATS 2022
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels ICLR 2022
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients ICLR 2022
Exploiting Class Activation Value for Partial-Label Learning ICLR 2022
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning ICLR 2022
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data ICLR 2022
To Smooth or Not? When Label Smoothing Meets Noisy Labels ICML 2022
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum ICML 2022
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests ICML 2022
Towards Adversarially Robust Deep Image Denoising IJCAI 2022
Fast and Robust Rank Aggregation against Model Misspecification JMLR 2022
Learning from Noisy Pairwise Similarity and Unlabeled Data JMLR 2022
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification ICML 2021
Provably End-to-end Label-noise Learning without Anchor Points ICML 2021
Probabilistic Margins for Instance Reweighting in Adversarial Training NIPS 2021
Loss function based second-order Jensen inequality and its application to particle variational inference NIPS 2021
Classification with Rejection Based on Cost-sensitive Classification ICML 2021
Large-Margin Contrastive Learning with Distance Polarization Regularizer ICML 2021
Lower-Bounded Proper Losses for Weakly Supervised Classification ICML 2021
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective CVPR 2021
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks ICML 2021
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning EACL 2021
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection ICML 2021
Confidence Scores Make Instance-dependent Label-noise Learning Possible ICML 2021
Geometry-aware Instance-reweighted Adversarial Training ICLR 2021
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences ICML 2021
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization ICML 2021
Learning from Similarity-Confidence Data ICML 2021
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima ICLR 2021
Learning Diverse-Structured Networks for Adversarial Robustness ICML 2021
Pointwise Binary Classification with Pairwise Confidence Comparisons ICML 2021
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization ICML 2021
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation UAI 2021
Robust Imitation Learning from Noisy Demonstrations AISTATS 2021
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation AISTATS 2021
Ξ³-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator AISTATS 2021
A unified view of likelihood ratio and reparameterization gradients AISTATS 2021
Progressive Identification of True Labels for Partial-Label Learning ICML 2020
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust ICML 2020
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels ICML 2020
Learning with Multiple Complementary Labels ICML 2020
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics ICML 2020
A One-step Approach to Covariate Shift Adaptation ACML 2020
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach AISTATS 2020
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification AISTATS 2020
Rethinking Importance Weighting for Deep Learning under Distribution Shift NIPS 2020
Provably Consistent Partial-Label Learning NIPS 2020
Calibrated Surrogate Losses for Adversarially Robust Classification COLT 2020
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring NIPS 2020
Learning from Aggregate Observations NIPS 2020
Part-dependent Label Noise: Towards Instance-dependent Label Noise NIPS 2020
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning NIPS 2020
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators NIPS 2020
Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes WACV 2020
Binary Classification from Positive Data with Skewed Confidence IJCAI 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger ICML 2020
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis ICML 2020
Few-shot Domain Adaptation by Causal Mechanism Transfer ICML 2020
Variational Imitation Learning with Diverse-quality Demonstrations ICML 2020
Online Dense Subgraph Discovery via Blurred-Graph Feedback ICML 2020
How does Disagreement Help Generalization against Label Corruption? ICML 2019
Clipped Matrix Completion: A Remedy for Ceiling Effects AAAI 2019
Imitation Learning from Imperfect Demonstration ICML 2019
Uncoupled Regression from Pairwise Comparison Data NIPS 2019
On the Calibration of Multiclass Classification with Rejection NIPS 2019
Dueling Bandits with Qualitative Feedback AAAI 2019
Learning Only from Relevant Keywords and Unlabeled Documents IJCNLP 2019
On Symmetric Losses for Learning from Corrupted Labels ICML 2019
Zero-shot Domain Adaptation Based on Attribute Information ACML 2019
Complementary-Label Learning for Arbitrary Losses and Models ICML 2019
Classification from Positive, Unlabeled and Biased Negative Data ICML 2019
BΓ©zier Simplex Fitting: Describing Pareto Fronts ofΒ΄ Simplicial Problems with Small Samples in Multi-Objective Optimization AAAI 2019
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data ICLR 2019
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization ICLR 2019
Are Anchor Points Really Indispensable in Label-Noise Learning? NIPS 2019
Learning Only from Relevant Keywords and Unlabeled Documents EMNLP 2019
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy AAAI 2019
Bayesian Posterior Approximation via Greedy Particle Optimization AAAI 2019
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model ICML 2018
Variational Inference based on Robust Divergences AISTATS 2018
A fully adaptive algorithm for pure exploration in linear bandits AISTATS 2018
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling AISTATS 2018
Guide Actor-Critic for Continuous Control ICLR 2018
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios JMLR 2018
Binary Classification from Positive-Confidence Data NIPS 2018
Masking: A New Perspective of Noisy Supervision NIPS 2018
Co-teaching: Robust training of deep neural networks with extremely noisy labels NIPS 2018
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces NIPS 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks NIPS 2018
Uplift Modeling from Separate Labels NIPS 2018
Classification from Pairwise Similarity and Unlabeled Data ICML 2018
Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML 2018
Generative Local Metric Learning for Kernel Regression NIPS 2017
Whitening-Free Least-Squares Non-Gaussian Component Analysis ACML 2017
Expectation Propagation for t-Exponential Family Using q-Algebra NIPS 2017
Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios AISTATS 2017
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data ICML 2017
Learning from Complementary Labels NIPS 2017
Learning Discrete Representations via Information Maximizing Self-Augmented Training ICML 2017
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds AISTATS 2017
Positive-Unlabeled Learning with Non-Negative Risk Estimator NIPS 2017
Structure Learning of Partitioned Markov Networks ICML 2016
Multitask Principal Component Analysis ACML 2016
Non-Gaussian Component Analysis with Log-Density Gradient Estimation AISTATS 2016
Geometry-aware stationary subspace analysis ACML 2016
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning NIPS 2016
Continuous Target Shift Adaptation in Supervised Learning ACML 2015
Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices ACML 2015
Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning IJCAI 2015
Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA JMLR 2015
Convex Formulation for Learning from Positive and Unlabeled Data ICML 2015
Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation ACML 2015
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation AISTATS 2015
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities ACML 2015
Class-prior Estimation for Learning from Positive and Unlabeled Data ACML 2015
Multitask learning meets tensor factorization: task imputation via convex optimization NIPS 2014
Outlier Path: A Homotopy Algorithm for Robust SVM ICML 2014
Transductive Learning with Multi-class Volume Approximation ICML 2014
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence AISTATS 2014
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS 2014
Analysis of Learning from Positive and Unlabeled Data NIPS 2014
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP NIPS 2014
Parametric Task Learning NIPS 2013
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines ICML 2013
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning ICML 2013
Maximum Volume Clustering: A New Discriminative Clustering Approach JMLR 2013
Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization JMLR 2013
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering NIPS 2013
Density-Difference Estimation NIPS 2012
Perfect Dimensionality Recovery by Variational Bayesian PCA NIPS 2012
Sparse Additive Matrix Factorization for Robust PCA and Its Generalization ACML 2012
Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness AISTATS 2012
Cross-Domain Object Matching with Model Selection AISTATS 2011
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent NIPS 2011
Maximum Volume Clustering AISTATS 2011
Theoretical Analysis of Bayesian Matrix Factorization JMLR 2011
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin JMLR 2011
Relative Density-Ratio Estimation for Robust Distribution Comparison NIPS 2011
Analysis and Improvement of Policy Gradient Estimation NIPS 2011
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation JMLR 2011
Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information ACML 2011
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification NIPS 2011
Conditional Density Estimation via Least-Squares Density Ratio Estimation AISTATS 2010
Global Analytic Solution for Variational Bayesian Matrix Factorization NIPS 2010
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation AISTATS 2010
Single versus Multiple Sorting in All Pairs Similarity Search ACML 2010
A Least-squares Approach to Direct Importance Estimation JMLR 2009
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection NIPS 2008
Covariate Shift Adaptation by Importance Weighted Cross Validation JMLR 2007
Multi-Task Learning via Conic Programming NIPS 2007
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation NIPS 2007
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis JMLR 2007
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error JMLR 2006
Mixture Regression for Covariate Shift NIPS 2006
In Search of Non-Gaussian Components of a High-Dimensional Distribution JMLR 2006
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces JMLR 2002