論文表記の著者名
Masashi Sugiyama
東京大学Machine Learning2022–2026 LIVE
対象論文 72 本
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対象論文
論文一覧
- Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
- Bilateral Information-aware Test-time Adaptation for Vision-Language Models
- CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Vision-Language Model
- Decoupling the Class Label and the Target Concept in Machine Unlearning
- Learning from Label Proportions via Proportional Value Classification
- Practical estimation of the optimal classification error with soft labels and calibration
- Rethinking Consistent Multi-Label Classification Under Inexact Supervision
- Towards Scalable Oversight via Partitioned Human Supervision
- Towards Understanding Valuable Preference Data for Large Language Model Alignment
- Unlocking the Power of Co-Occurrence in CLIP: A DualPrompt-Driven Method for Training-Free Zero-Shot Multi-Label Classification
- Weak-to-Strong Diffusion with Reflection
- Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
- Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
- Incorporating Importance Weighting in Optimal Transport Based Domain Alignment
- Positive-Unlabeled Learning with Extreme Scarcity of Labeled Positives
- Positive–Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
- Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
- Learning View-invariant World Models for Visual Robotic Manipulation
- Realistic Evaluation of Deep Partial-Label Learning Algorithms
- Sharpness-Aware Black-Box Optimization
- Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
- Towards Out-of-Modal Generalization without Instance-level Modal Correspondence
- Adaptive Localization of Knowledge Negation for Continual LLM Unlearning
- Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability
- Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models
- Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update
- The Adaptive Complexity of Minimizing Relative Fisher Information
- Accurate Forgetting for Heterogeneous Federated Continual Learning
- Robust Similarity Learning with Difference Alignment Regularization
- Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
- A General Framework for Learning from Weak Supervision
- Balancing Similarity and Complementarity for Federated Learning
- Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
- Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation
- Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
- Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
- Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
- Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
- Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
- Slight Corruption in Pre-training Data Makes Better Diffusion Models
- Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment
- What Makes Partial-Label Learning Algorithms Effective?
- Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
- Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
- A Category-theoretical Meta-analysis of Definitions of Disentanglement
- Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
- GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
- Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
- Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
- Binary Classification with Confidence Difference
- Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
- Distributional Pareto-Optimal Multi-Objective Reinforcement Learning
- Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
- Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
- Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
- Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
- Imitation Learning from Vague Feedback
- On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
- Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
- Exploiting Class Activation Value for Partial-Label Learning
- Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients
- Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
- Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
- Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
- Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
- Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
- To Smooth or Not? When Label Smoothing Meets Noisy Labels
- Adapting to Online Label Shift with Provable Guarantees
- Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
- Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses
- Learning Contrastive Embedding in Low-Dimensional Space
- Synergy-of-Experts: Collaborate to Improve Adversarial Robustness