157 citations · 248 across the 24 of their papers we have counts for
8 papers · 1 filter
Mixup Regularization: A Probabilistic Perspective
Yousef El-Laham, Niccolò Dalmasso, Svitlana Vyetrenko +2
In recent years, mixup regularization has gained popularity as an effective way to improve the generalization performance of deep learning models by training on convex combinations…
Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction
Harit Vishwakarma, Alan Mishler, Thomas Cook +3
Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, incorrect…
Auditing and Enforcing Conditional Fairness via Optimal Transport
Mohsen Ghassemi, Alan Mishler, Niccolo Dalmasso +4
Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features.…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
Zikai Xiong, Niccolò Dalmasso, Alan Mishler +3
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…
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi +3
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the sel…
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…
Explicit Group Sparse Projection with Applications to Deep Learning and NMF
Riyasat Ohib, Nicolas Gillis, Niccolò Dalmasso +3
We design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure (an affine function of…