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Robust Learning

72 papers

Papers per year

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Papers

A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise AAAI 2023
Corruption-Tolerant Algorithms for Generalized Linear Models AAAI 2023
Denoising Multi-Similarity Formulation: A Self-Paced Curriculum-Driven Approach for Robust Metric Learning AAAI 2023
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints AISTATS 2023
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning CVPR 2023
Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering EMNLP 2023
Byzantine-tolerant federated Gaussian process regression for streaming data NIPS 2022
Overparameterization from Computational Constraints NIPS 2022
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering NIPS 2022
Distributionally robust weighted k-nearest neighbors NIPS 2022
Robust Reinforcement Learning using Offline Data NIPS 2022
Anytime Guarantees under Heavy-Tailed Data AAAI 2022
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data AAAI 2022
Robust Product Classification with Instance-Dependent Noise ACL 2022
3DeformRS: Certifying Spatial Deformations on Point Clouds CVPR 2022
On Learning Contrastive Representations for Learning With Noisy Labels CVPR 2022
STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing EMNLP 2022
Robust Kernel Density Estimation with Median-of-Means principle ICML 2022
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers NIPS 2021
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries AAAI 2021
Learning from Noisy Labels with Complementary Loss Functions AAAI 2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model AAAI 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