conftrace_

robust learning

120 papers

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Co-occurring keywords

label noise (498) noisy label (321) adversarial learning (1595) sample complexity (1169) robust optimization (307) adversarial robustness (1341) pac learning (283) domain adaptation (4595) graph neural network (3962) deep neural network (1803)

Papers

Learning Deep Neural Networks under Agnostic Corrupted Supervision ICML 2021
Robust Imitation Learning from Noisy Demonstrations AISTATS 2021
DualGraph: A Graph-Based Method for Reasoning About Label Noise CVPR 2021
Jo-SRC: A Contrastive Approach for Combating Noisy Labels CVPR 2021
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks CVPR 2021
Adversarially Robust Change Point Detection NIPS 2021
Robust learning under clean-label attack COLT 2021
Outlier-Robust Learning of Ising Models Under Dobrushin’s Condition COLT 2021
Robust Learning under Strong Noise via SQs AISTATS 2021
Improving Unsupervised Image Clustering With Robust Learning CVPR 2021
Label Distribution for Learning with Noisy Labels IJCAI 2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning NIPS 2020
Robust high dimensional learning for Lipschitz and convex losses JMLR 2020
Leakage-Robust Classifier via Mask-Enhanced Training (Student Abstract) AAAI 2020
Black-box Certification and Learning under Adversarial Perturbations ICML 2020
Recovery of sparse linear classifiers from mixture of responses NIPS 2020
Data Valuation using Reinforcement Learning ICML 2020
Robust guarantees for learning an autoregressive filter ALT 2020
New Efficient Multi-Spike Learning for Fast Processing and Robust Learning AAAI 2020
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization EMNLP 2020
A General Method for Robust Learning from Batches NIPS 2020
Robust Federated Learning via Collaborative Machine Teaching AAAI 2020
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust ICML 2020
Improved Generalization Bounds for Robust Learning ALT 2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback ICML 2019