Niccolò Dalmasso

According to our database, Niccolò Dalmasso authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic.

Matteo Sordello, Niccolò Dalmasso, Hangfeng He, 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

CoRR, 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, Deepak Paramanand, Natraj Raman, Mikhail Solonin, Srijan Sood, Svitlana Vyetrenko, Haibei Zhu, Manuela Veloso, Tucker Balch

CoRR, 2024

FairWASP: Fast and Optimal Fair Wasserstein Pre-processing.

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

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Fair Wasserstein Coresets.

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

CoRR, 2023

Deep Gaussian mixture ensembles.

Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Efficient Event Series Data Modeling via First-Order Constrained Optimization.

Niccolò Dalmasso, Renbo Zhao, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

Proceedings of the 4th ACM International Conference on AI in Finance, 2023

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

Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe.

Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

CoRR, 2022

Differentially Private Learning of Hawkes Processes.

Mohsen Ghassemi, Eleonora Kreacic, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

CoRR, 2022

Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings.

Alan Mishler, Niccolò Dalmasso

CoRR, 2022

Online Learning for Mixture of Multivariate Hawkes Processes.

Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba, Vamsi K. Potluru, Tucker Balch, Sameena Shah, Manuela Veloso

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

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

Diagnostics for conditional density models and Bayesian inference algorithms.

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

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

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

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

Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting.

Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020