Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Kreacic, Eleonora"'
Autor:
Selvi, Aras, Kreacic, Eleonora, Ghassemi, Mohsen, Potluru, Vamsi, Balch, Tucker, Veloso, Manuela
Empirical risk minimization often fails to provide robustness against adversarial attacks in test data, causing poor out-of-sample performance. Adversarially robust optimization (ARO) has thus emerged as the de facto standard for obtaining models tha
Externí odkaz:
http://arxiv.org/abs/2407.13625
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
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:
Zeng, Sihan, Bhatt, Sujay, Kreacic, Eleonora, Hassanzadeh, Parisa, Koppel, Alec, Ganesh, Sumitra
We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for revenue maximization in auctions, we consider welfare maximization as
Externí odkaz:
http://arxiv.org/abs/2311.10927
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
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based approaches have emerged in recent years for answering statistical queri
Externí odkaz:
http://arxiv.org/abs/2306.13211
We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to design fair allocation algorithms that exhaust the available resource bud
Externí odkaz:
http://arxiv.org/abs/2301.03758
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
Autor:
Kreacic, Eleonora
We study certain questions related to the performance of the Karp-Sipser algorithm on the sparse Erdös-Rényi random graph. The Karp-Sipser algorithm, introduced by Karp and Sipser [34] is a greedy algorithm which aims to obtain a near-maximum mat
Externí odkaz:
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757704