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Robust Learning

68 directly classified papers

Papers per year

Papers

Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering EMNLP 2023
Efficient Testable Learning of Halfspaces with Adversarial Label Noise NIPS 2023
Anytime Guarantees under Heavy-Tailed Data AAAI 2022
3DeformRS: Certifying Spatial Deformations on Point Clouds CVPR 2022
Robust Kernel Density Estimation with Median-of-Means principle ICML 2022
Robust Reinforcement Learning using Offline Data NIPS 2022
STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing EMNLP 2022
Distributionally robust weighted k-nearest neighbors NIPS 2022
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering NIPS 2022
Overparameterization from Computational Constraints NIPS 2022
Byzantine-tolerant federated Gaussian process regression for streaming data NIPS 2022
On Learning Contrastive Representations for Learning With Noisy Labels CVPR 2022
Robust Product Classification with Instance-Dependent Noise ACL 2022
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data AAAI 2022
Outlier-Robust Optimal Transport ICML 2021
DualGraph: A Graph-Based Method for Reasoning About Label Noise CVPR 2021
Jo-SRC: A Contrastive Approach for Combating Noisy Labels CVPR 2021
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions CVPR 2021
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks CVPR 2021
Learning from Noisy Labels with Complementary Loss Functions AAAI 2021
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries AAAI 2021
Instance-adaptive training with noise-robust losses against noisy labels EMNLP 2021
Consistent regression when oblivious outliers overwhelm ICML 2021
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers NIPS 2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model AAAI 2021