Jiaheng Wei

Advances-in-Label-Noise-Learning. A curated (most recent) list of resources for Learning with Noisy Labels

Robust-f-divergence-measures. [ICLR2021] Official Pytorch implementation of "When Optimizing f-Divergence is Robust with Label noise"

Drops. [ICLR 2023] Official Tensorflow implementation of "Distributionally Robust Post-hoc Classifiers under Prior Shifts"

Credible-sample-elicitation. [AISTATS2021] Official implementation of "Sample Elicitation"

Multi-class-Peer-Loss-functions. Learning with Noisy Labels by adopting a peer prediction loss function (deep learning & multi-class version).

CHALE. Controlled HALlucination-Evaluation (CHALE) Question-Answering Dataset

Model-Elicitation. [DAI 2023] Official Pytorch implementation of "Auditing for federated learning: A model elicitation approach"