157 citations · 248 across the 24 of their papers we have counts for
24 papers
Breaking Distortion-free Watermarks in Large Language Models
Shayleen Reynolds, Hengzhi He, Dung Daniel T. Ngo +5
In recent years, LLM watermarking has emerged as an attractive safeguard against AI-generated content, with promising applications in many real-world domains. However, there are gr…
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…
Fair Wasserstein Coresets
Zikai Xiong, Niccolò Dalmasso, Shubham Sharma +5
Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets.…
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…
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…
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…
Online Learning for Mixture of Multivariate Hawkes Processes
Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba +4
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either…
Differentially Private Learning of Hawkes Processes
Mohsen Ghassemi, Eleonora Kreačić, Niccolò Dalmasso +3
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history…
Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance
Trey McNeely, Pavel Khokhlov, Niccolo Dalmasso +2
Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to sho…
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…
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
Niccolò Dalmasso, Luca Masserano, David Zhao +2
Many areas of science rely on simulators that implicitly encode intractable likelihood functions of complex systems. Classical statistical methods are poorly suited for these so-ca…
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…
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that…
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…
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
Niccolò Dalmasso, Taylor Pospisil, Ann B. Lee +3
It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Si…
A Flexible Pipeline for Prediction of Tropical Cyclone Paths
Niccolò Dalmasso, Robin Dunn, Benjamin LeRoy +1
Hurricanes and, more generally, tropical cyclones (TCs) are rare, complex natural phenomena of both scientific and public interest. The importance of understanding TCs in a changin…
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki +3
Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist…
Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder
Stephen M. Feeney, Daniel J. Mortlock, Niccolò Dalmasso
Estimates of the Hubble constant, , from the distance ladder and the cosmic microwave background (CMB) differ at the 3- level, indicating a potential issue with the s…