Zobrazeno 1 - 10
of 676
pro vyhledávání: '"TRAN, CUONG"'
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in privacy-by-design principles, data minimization has been endo
Externí odkaz:
http://arxiv.org/abs/2405.19471
Autor:
Das, Saswat, Romanelli, Marco, Tran, Cuong, Reza, Zarreen, Kailkhura, Bhavya, Fioretto, Ferdinando
Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in capturing the s
Externí odkaz:
http://arxiv.org/abs/2405.18572
Autor:
Do, Binh N, Tran, Tien V, Phan, Dung T, Nguyen, Hoang C, Nguyen, Thao T P, Nguyen, Huu C, Ha, Tung H, Dao, Hung K, Trinh, Manh V, Do, Thinh V, Nguyen, Hung Q, Vo, Tam T, Nguyen, Nhan P T, Tran, Cuong Q, Tran, Khanh V, Duong, Trang T, Pham, Hai X, Nguyen, Lam V, Nguyen, Kien T, Chang, Peter W S, Duong, Tuyen Van
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 11, p e22894 (2020)
BackgroundThe COVID-19 pandemic has imposed a heavy burden on health care systems and governments. Health literacy (HL) and eHealth literacy (as measured by the eHealth Literacy Scale [eHEALS]) are recognized as strategic public health elements but t
Externí odkaz:
https://doaj.org/article/44e52ec8321b4113977a6860577da930
Autor:
Nelaturu, Sree Harsha, Ravichandran, Nishaanth Kanna, Tran, Cuong, Hooker, Sara, Fioretto, Ferdinando
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly problematic in contexts like ML-as-a-service platforms, where users often
Externí odkaz:
http://arxiv.org/abs/2312.03886
Autor:
Tran, Cuong, Fioretto, Ferdinando
In domains with high stakes such as law, recruitment, and healthcare, learning models frequently rely on sensitive user data for inference, necessitating the complete set of features. This not only poses significant privacy risks for individuals but
Externí odkaz:
http://arxiv.org/abs/2305.17593
Autor:
Tran, Cuong, Fioretto, Ferdinando
The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models and a "student" model. The student model learns to predict an output
Externí odkaz:
http://arxiv.org/abs/2305.11807
Autor:
Tran, Cuong, Fioretto, Ferdinando
A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' information to carry out inference. Further, the complete set of features is ty
Externí odkaz:
http://arxiv.org/abs/2302.00077
The remarkable performance of deep learning models and their applications in consequential domains (e.g., facial recognition) introduces important challenges at the intersection of equity and security. Fairness and robustness are two desired notions
Externí odkaz:
http://arxiv.org/abs/2211.11835
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on
Externí odkaz:
http://arxiv.org/abs/2205.13574
A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the group attributes
Externí odkaz:
http://arxiv.org/abs/2204.05157