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Charles Lu

2 accepted papers

2024

Data Acquisition via Experimental Design for Data Markets

NeurIPS 2024poster

The acquisition of training data is crucial for machine learning applications. Data markets can increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data providers to join the market. A major challenge for a data buyer in such a market is ch…

Cited by 1SourcePDFScholar
2023

Federated Conformal Predictors for Distributed Uncertainty Quantification

ICML 2023poster

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal prediction to the federated learning setting. The main c…