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ORCIDNiccolò Dalmasso
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2020 – today
- 2024
[j3]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)- [c7]Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing. AAAI 2024: 16120-16128 - [i11]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 abs/2401.00081 (2024) - 2023
- [c6]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: 463-471 - [c5]Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko:
Deep Gaussian mixture ensembles. UAI 2023: 549-559 - [i10]Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko:
Deep Gaussian Mixture Ensembles. CoRR abs/2306.07235 (2023) - [i9]Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing. CoRR abs/2311.00109 (2023) - [i8]Zikai Xiong, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
Fair Wasserstein Coresets. CoRR abs/2311.05436 (2023) - 2022
- [j2]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) - [c4]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: 506-513 - [i7]Alan Mishler, Niccolò Dalmasso:
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings. CoRR abs/2202.05049 (2022) - [i6]Mohsen Ghassemi, Eleonora Kreacic, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
Differentially Private Learning of Hawkes Processes. CoRR abs/2207.13741 (2022) - [i5]Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba, Vamsi K. Potluru, Sameena Shah, Tucker Balch, Manuela Veloso:
Online Learning for Mixture of Multivariate Hawkes Processes. CoRR abs/2208.07961 (2022) - [i4]Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe. CoRR abs/2212.06081 (2022) - 2021
- [c3]David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Diagnostics for conditional density models and Bayesian inference algorithms. UAI 2021: 1830-1840 - [i3]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 abs/2107.03920 (2021) - 2020
- [j1]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: 100362 (2020) - [c2]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: 3349-3361 - [c1]Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. ICML 2020: 2323-2334 - [i2]Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. CoRR abs/2002.10399 (2020) - [i1]Trey McNeely, Niccolò Dalmasso, Kimberly M. Wood, Ann B. Lee:
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning. CoRR abs/2010.05783 (2020)
Coauthor Index
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last updated on 2024-08-10 01:26 CEST by the dblp team
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