Leverage Latent Label Distributions for Partial Label Learning [IJCAI'18]. [LALO]
Partial Label Learning with Self-Guided Retraining [AAAI'19]. [SURE]
Progressive Identification of True Labels for Partial-Label Learning [ICML'20]. [PRODEN]
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization [CVPR'20]. [JoCoR]
Embedding-Augmented Generalized Matrix Factorization for Recommendation with Implicit Feedback [IEEE IS'21]. [AGMF]
Can Cross Entropy Loss Be Robust to Label Noise [IJCAI'20]. [TCE]
Learning with Multiple Complementary Labels [ICML'20]. [LMCL]
Provably Consistent Partial-Label Learning [NeurIPS'20]. [RC & CC]
Learning from Complementary Labels via Partial-Output Consistency Regularization [IJCAI'21]. [CLL_POCR]
Pointwise Binary Classification with Pairwise Confidence Comparisons [ICML'21]. [Pcomp]
Learning from Similarity-Confidence Data [ICML'21]. [Sconf]
Multiple-Instance Learning from Similar and Dissimilar Bags [KDD'21]. [SDMIL]
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training [NeurIPS'21]. [Delusive-Adversary]
With False Friends Like These, Who Can Notice Mistakes [AAAI'22]. [False-Friends]
PiCO: Contrastive Label Disambiguation for Partial-Label Learning [ICLR'22]. [PiCO]
Exploiting Class Activation Value for Partial-Label Learning [ICLR'22]. [CAVL]
Mitigating Neural Network Overconfidence with Logit Normalization [ICML'22]. [LogitNorm]
Open-Sampling: Exploring Out-of-Distribution Data for Re-balancing Long-tailed Datasets [ICML'22]. [Open-sampling]
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning [TMLR'22]. [SemiNLL]
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning [NeurIPS'22]. [SoLar]
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses [NeurIPS'22]. [CwR]
Partial-Label Regression [AAAI'23]. [PLR]
A Universal Unbiased Method for Classification from Aggregate Observations [ICML'23]. [UUM]
Weakly Supervised Regression with Interval Targets [ICML'23]. [WSRIT]
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction [ICML'23]. [LogitClip]
Consistent Complementary-Label Learning via Order-Preserving Losses [AISTATS'23]. [OPCLL]
Binary Classification with Confidence Difference [NeurIPS'23]. [ConfDiff]
Regression with Cost-based Rejection [NeurIPS'23]. [RcR]