Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Dalmasso, Niccolo"'
Autor:
Ghassemi, Mohsen, Mishler, Alan, Dalmasso, Niccolo, Zhang, Luhao, Potluru, Vamsi K., Balch, Tucker, Veloso, Manuela
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. Many algorithmic fairness techniques exist to target demographic pari
Externí odkaz:
http://arxiv.org/abs/2410.14029
Autor:
Potluru, Vamsi K., Borrajo, Daniel, Coletta, Andrea, Dalmasso, Niccolò, El-Laham, Yousef, Fons, Elizabeth, Ghassemi, Mohsen, Gopalakrishnan, Sriram, Gosai, Vikesh, Kreačić, Eleonora, Mani, Ganapathy, Obitayo, Saheed, Paramanand, Deepak, Raman, Natraj, Solonin, Mikhail, Sood, Srijan, Vyetrenko, Svitlana, Zhu, Haibei, Veloso, Manuela, Balch, Tucker
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide
Externí odkaz:
http://arxiv.org/abs/2401.00081
Autor:
Xiong, Zikai, Dalmasso, Niccolò, Sharma, Shubham, Lecue, Freddy, Magazzeni, Daniele, Potluru, Vamsi K., Balch, Tucker, Veloso, Manuela
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. At the same time, machine learning is being increasingly applied to de
Externí odkaz:
http://arxiv.org/abs/2311.05436
Autor:
Xiong, Zikai, Dalmasso, Niccolò, Mishler, Alan, Potluru, Vamsi K., Balch, Tucker, Veloso, Manuela
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 in multiple downstream applications by different users, which means i
Externí odkaz:
http://arxiv.org/abs/2311.00109
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data generating process follows that
Externí odkaz:
http://arxiv.org/abs/2306.07235
Autor:
Zhao, Renbo, Dalmasso, Niccolò, Ghassemi, Mohsen, Potluru, Vamsi K., Balch, Tucker, Veloso, Manuela
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 self and cross-excitation between different types of events and have been
Externí odkaz:
http://arxiv.org/abs/2212.06081
Autor:
Ghassemi, Mohsen, Dalmasso, Niccolò, Lamba, Simran, Potluru, Vamsi K., Shah, Sameena, Balch, Tucker, Veloso, Manuela
Publikováno v:
ICAIF 22: 3rd ACM International Conference on AI in Finance, November 2022, Pages 506-513
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 model the rich interaction between the events or the latent cluster o
Externí odkaz:
http://arxiv.org/abs/2208.07961
Autor:
Ghassemi, Mohsen, Kreačić, Eleonora, Dalmasso, Niccolò, Potluru, Vamsi K., Balch, Tucker, Veloso, Manuela
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 going back decades, some of their properties, such as sample complexi
Externí odkaz:
http://arxiv.org/abs/2207.13741
Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subseque
Externí odkaz:
http://arxiv.org/abs/2206.00067
Autor:
Mishler, Alan, Dalmasso, Niccolò
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 distribution shift: a predictor which is trained to satisfy one of thes
Externí odkaz:
http://arxiv.org/abs/2202.05049