Algorithms for abstention, calibration and domain adaptation to label shift.
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Updated
Nov 14, 2020 - Python
Algorithms for abstention, calibration and domain adaptation to label shift.
A curated list of Robust Machine Learning papers/articles and recent advancements.
A curated list of Distribution Shift papers/articles and recent advancements.
LAMDA: Label Matching Deep Domain Adaptation - ICML 2021
"Mapping conditional distributions for domain adaptation under generalized target shift" - ICLR2022
(ACL 2026 Main) LLMSurgeon recovers the pretraining data mixture of any LLM from only its generated text — no weights, no training data. A calibrated domain classifier plus label-shift correction de-blurs biased predictions. Ships with LLMScan, a benchmark on 8 open-source LLMs.
Bayesian Quantification with Black-Box Estimators
Coping with Label Shift via Distributionally Robust Optimisation
ML model monitoring is not just drift detection. This repo benchmarks PSI, KS, MMD, and ADWIN across real failure scenarios like data drift, label shift, and model decay in production.
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