Niccolò Dalmasso — accepted AI-conference papers

2024

FairWASP: Fast and Optimal Fair Wasserstein Pre-processing

Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

AAAI 2024technical

Recent years have seen a surge of machine learning approaches aimed at reducing disparities in model outputs across different subgroups. In many settings, training data may be used in multiple downstream applications by different users, which means it may be most effective to intervene on the traini…

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2021

Diagnostics for conditional density models and Bayesian inference algorithms

David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee

UAI 2021poster

There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional features, are an integral part of uncertainty quantification in prediction and Bayesian inference. However, it is challenging…

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