Zobrazeno 1 - 10
of 3 359
pro vyhledávání: '"Vamsi, K."'
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
Recent developments in generative models have demonstrated its ability to create high-quality synthetic data. However, the pervasiveness of synthetic content online also brings forth growing concerns that it can be used for malicious purposes. To ens
Externí odkaz:
http://arxiv.org/abs/2409.14700
Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or quantiles). Existing literature only focuses on protecting a single con
Externí odkaz:
http://arxiv.org/abs/2405.13804
Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well. Such data may arise from client informatio
Externí odkaz:
http://arxiv.org/abs/2403.14724
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single family of generative models. They provided limited insights on how synt
Externí odkaz:
http://arxiv.org/abs/2401.00974
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
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 38(14), 16120-16128, 2024
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
Autor:
Wei, Rongzhe, Kreačić, Eleonora, Wang, Haoyu, Yin, Haoteng, Chien, Eli, Potluru, Vamsi K., Li, Pan
Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical evaluations on these models, there has been a gap in providing a mathema
Externí odkaz:
http://arxiv.org/abs/2310.15524
Large-scale graphs with node attributes are increasingly common in various real-world applications. Creating synthetic, attribute-rich graphs that mirror real-world examples is crucial, especially for sharing graph data for analysis and developing le
Externí odkaz:
http://arxiv.org/abs/2310.13833