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Papers
Variation-Bounded Loss for Noise-Tolerant Learning
AAAI 2026
Learning Robust XGBoost Ensembles for Regression Tasks
UAI 2025
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
ICLR 2024
Robust Loss Functions for Training Decision Trees with Noisy Labels
AAAI 2024
Mitigating Memorization of Noisy Labels via Regularization between Representations
ICLR 2023
PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions
SemEval 2022
Robustness and Reliability When Training With Noisy Labels
AISTATS 2022
Learning from Noisy Labels with Complementary Loss Functions
AAAI 2021
Attribute Noise Robust Binary Classification (Student Abstract)
AAAI 2020
Can Cross Entropy Loss Be Robust to Label Noise?
IJCAI 2020
Normalized Loss Functions for Deep Learning with Noisy Labels
ICML 2020
A General and Adaptive Robust Loss Function
CVPR 2019
Forest-type Regression with General Losses and Robust Forest
ICML 2017
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
NeurIPS 2010