Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. - Niccolò Dalmasso; Ann B. Lee; Rafael Izbicki; Taylor Pospisil; Ilmun Kim; Chieh-An Lin (AISTATS, 2020)

Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. - Niccolò Dalmasso; Rafael Izbicki; Ann B. Lee (ICML, 2020)

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; Rafael Izbicki; Peter E. Freeman; Alex I. Malz (Astron. Comput., 2020)

Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning. - Trey McNeely; Niccolò Dalmasso; Kimberly M. Wood; Ann B. Lee (CoRR, 2020)

Diagnostics for conditional density models and Bayesian inference algorithms. - David Zhao; Niccolò Dalmasso; Rafael Izbicki; Ann B. Lee (UAI, 2021)

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification. - Niccolò Dalmasso; David Zhao; Rafael Izbicki; Ann B. Lee (CoRR, 2021)

Online Learning for Mixture of Multivariate Hawkes Processes. - Mohsen Ghassemi; Niccolò Dalmasso; Simran Lamba; Vamsi K. Potluru; Tucker Balch; Sameena Shah; Manuela Veloso (ICAIF, 2022)

Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings. - Alan Mishler; Niccolò Dalmasso (CoRR, 2022)

Differentially Private Learning of Hawkes Processes. - Mohsen Ghassemi; Eleonora Kreacic; Niccolò Dalmasso; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (CoRR, 2022)

Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe. - Renbo Zhao; Niccolò Dalmasso; Mohsen Ghassemi; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (CoRR, 2022)

Explicit Group Sparse Projection with Applications to Deep Learning and NMF. - Riyasat Ohib; Nicolas Gillis; Niccolò Dalmasso; Sameena Shah; Vamsi K. Potluru; Sergey M. Plis (Trans. Mach. Learn. Res., 2022)

Efficient Event Series Data Modeling via First-Order Constrained Optimization. - Niccolò Dalmasso; Renbo Zhao; Mohsen Ghassemi; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (ICAIF, 2023)

Deep Gaussian mixture ensembles. - Yousef El-Laham; Niccolò Dalmasso; Elizabeth Fons; Svitlana Vyetrenko (UAI, 2023)

Fair Wasserstein Coresets. - Zikai Xiong; Niccolò Dalmasso; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (CoRR, 2023)

FairWASP: Fast and Optimal Fair Wasserstein Pre-processing. - Zikai Xiong; Niccolò Dalmasso; Alan Mishler; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (AAAI, 2024)

Fair Wasserstein Coresets. - Zikai Xiong; Niccolò Dalmasso; Shubham Sharma; Freddy Lécué; Daniele Magazzeni; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (NeurIPS, 2024)

Synthetic Data Applications in Finance. - Vamsi K. Potluru; Daniel Borrajo; Andrea Coletta; Niccolò Dalmasso; Yousef El-Laham; Elizabeth Fons; Mohsen Ghassemi; Sriram Gopalakrishnan; Vikesh Gosai; Eleonora Kreacic; Ganapathy Mani; Saheed Obitayo; et al. (CoRR, 2024)

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic. - Matteo Sordello; Niccolò Dalmasso; Hangfeng He 001; Weijie J. Su (Trans. Mach. Learn. Res., 2024)

Auditing and Enforcing Conditional Fairness via Optimal Transport. - Mohsen Ghassemi; Alan Mishler; Niccolò Dalmasso; Luhao Zhang; Vamsi K. Potluru; Tucker Balch; Manuela Veloso (AAAI, 2025)

Mixup Regularization: A Probabilistic Perspective. - Yousef El-Laham; Niccolò Dalmasso; Svitlana Vyetrenko; Vamsi K. Potluru; Manuela Veloso (UAI, 2025)

Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs. - Harit Vishwakarma; Alan Mishler; Thomas Cook; Niccolò Dalmasso; Natraj Raman; Sumitra Ganesh (CoRR, 2025)

Toward Breaking Watermarks in Distortion-free Large Language Models. - Shayleen Reynolds; Saheed Obitayo; Niccolò Dalmasso; Dung Daniel T. Ngo; Vamsi K. Potluru; Manuela Veloso (CoRR, 2025)