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Robust Learning
68 directly classified papers
Papers per year
2007: 1
2010: 1
2012: 1
2013: 1
2014: 1
2017: 2
2018: 1
2019: 3
2020: 7
2021: 11
2022: 12
2023: 11
2024: 14
2025: 2
Papers
Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling
AAAI 2025
Two Challenges, One Solution: Robust Multimodal Learning through Dynamic Modality Recognition and Enhancement
EMNLP 2025
Federated Learning with Extremely Noisy Clients via Negative Distillation
AAAI 2024
Collaborative Refining for Learning from Inaccurate Labels
NIPS 2024
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
NIPS 2024
Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning
EMNLP 2024
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
NIPS 2024
Robust variance-regularized risk minimization with concomitant scaling
AISTATS 2024
Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework
EMNLP 2024
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition
EMNLP 2024
Learning to Reweight for Generalizable Graph Neural Network
AAAI 2024
Active Reinforcement Learning for Robust Building Control
AAAI 2024
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
NIPS 2024
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
CVPR 2024
Learning from Noisy Labels via Conditional Distributionally Robust Optimization
NIPS 2024
Toward Robustness in Multi-Label Classification: A Data Augmentation Strategy against Imbalance and Noise
AAAI 2024
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning
CVPR 2023
Denoising Multi-Similarity Formulation: A Self-Paced Curriculum-Driven Approach for Robust Metric Learning
AAAI 2023
A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise
AAAI 2023
Combating Bilateral Edge Noise for Robust Link Prediction
NIPS 2023
Robust Domain Adaptation for Machine Reading Comprehension
AAAI 2023
Corruption-Tolerant Algorithms for Generalized Linear Models
AAAI 2023
Multiple Robust Learning for Recommendation
AAAI 2023
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints
AISTATS 2023
Denoising after Entropy-Based Debiasing a Robust Training Method for Dataset Bias with Noisy Labels
AAAI 2023
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