Niccolò Dalmasso

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 21

Top Topics

Hypothesis Test

Constrained Optimization

Conditional Density

Top Venues

Trans. Mach. Learn. Res.

Publications

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
Synthetic Data Applications in Finance. CoRR (2024)
Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing. AAAI (2024)
Matteo Sordello, Niccolò Dalmasso, Hangfeng He, Weijie J. Su
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic. Trans. Mach. Learn. Res. 2024 (2024)
Niccolò Dalmasso, Renbo Zhao, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
Efficient Event Series Data Modeling via First-Order Constrained Optimization. ICAIF (2023)
Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko
Deep Gaussian Mixture Ensembles. CoRR (2023)
Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing. CoRR (2023)
Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko
Deep Gaussian mixture ensembles. UAI (2023)
Zikai Xiong, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
Fair Wasserstein Coresets. CoRR (2023)
Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe. CoRR (2022)
Riyasat Ohib, Nicolas Gillis, Niccolò Dalmasso, Sameena Shah, Vamsi K. Potluru, Sergey M. Plis
Explicit Group Sparse Projection with Applications to Deep Learning and NMF. Trans. Mach. Learn. Res. 2022 (2022)
Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba, Vamsi K. Potluru, Sameena Shah, Tucker Balch, Manuela Veloso
Online Learning for Mixture of Multivariate Hawkes Processes. CoRR (2022)
Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba, Vamsi K. Potluru, Tucker Balch, Sameena Shah, Manuela Veloso
Online Learning for Mixture of Multivariate Hawkes Processes. ICAIF (2022)
Alan Mishler, Niccolò Dalmasso
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings. CoRR (2022)
Mohsen Ghassemi, Eleonora Kreacic, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
Differentially Private Learning of Hawkes Processes. CoRR (2022)
Niccolò Dalmasso, David Zhao, Rafael Izbicki, Ann B. Lee
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification. CoRR (2021)
David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
Diagnostics for conditional density models and Bayesian inference algorithms. UAI (2021)
Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. CoRR (2020)
Trey McNeely, Niccolò Dalmasso, Kimberly M. Wood, Ann B. Lee
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning. CoRR (2020)
Niccolò Dalmasso, Taylor Pospisil, Ann B. Lee, Rafael Izbicki, Peter E. Freeman, Alex I. Malz
Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference. Astron. Comput. 30 (2020)
Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim, Chieh-An Lin
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. AISTATS (2020)
Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. ICML (2020)