3 citations · 3 across the 5 of their papers we have counts for
10 papers
Deep Gaussian Mixture Ensembles
Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons +1
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleat…
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
Alan Mishler, Niccolò Dalmasso
Many popular algorithmic fairness measures depend on the joint distribution of predictions, outcomes, and a sensitive feature like race or gender. These measures are sensitive to d…
When the Oracle Misleads: Modeling the Consequences of Using Observable Rather than Potential Outcomes in Risk Assessment Instruments
Alan Mishler, Niccolò Dalmasso
Risk Assessment Instruments (RAIs) are widely used to forecast adverse outcomes in domains such as healthcare and criminal justice. RAIs are commonly trained on observational data…
Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki +1
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 featu…
HECT: High-Dimensional Ensemble Consistency Testing for Climate Models
Niccolò Dalmasso, Galen Vincent, Dorit Hammerling +1
Climate models play a crucial role in understanding the effect of environmental and man-made changes on climate to help mitigate climate risks and inform governmental decisions. La…
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
Trey McNeely, Niccolò Dalmasso, Kimberly M. Wood +1
Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestibl…