Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Baharlouei, Sina"'
Autor:
Baharlouei, Sina, Sabouri, Sadra
Fair graph clustering is crucial for ensuring equitable representation and treatment of diverse communities in network analysis. Traditional methods often ignore disparities among social, economic, and demographic groups, perpetuating biased outcomes
Externí odkaz:
http://arxiv.org/abs/2410.15233
Publikováno v:
ICLR 2024
Training and deploying machine learning models that meet fairness criteria for protected groups are fundamental in modern artificial intelligence. While numerous constraints and regularization terms have been proposed in the literature to promote fai
Externí odkaz:
http://arxiv.org/abs/2312.03259
Autor:
Baharlouei, Sina, Razaviyayn, Meisam
While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions. In the presence of distribution shifts, fair models ma
Externí odkaz:
http://arxiv.org/abs/2309.11682
Publikováno v:
International Conference on Artificial Intelligence and Statistics, PMLR 2023
This work concerns the development of deep networks that are certifiably robust to adversarial attacks. Joint robust classification-detection was recently introduced as a certified defense mechanism, where adversarial examples are either correctly cl
Externí odkaz:
http://arxiv.org/abs/2210.14410
Publikováno v:
Transaction on Machine Learning Research (TMLR), 09/2023
The ubiquity of missing values in real-world datasets poses a challenge for statistical inference and can prevent similar datasets from being analyzed in the same study, precluding many existing datasets from being used for new analyses. While an ext
Externí odkaz:
http://arxiv.org/abs/2109.00644
Publikováno v:
Transactions on Machine Learning Research, 2022
Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibility of fairness constraints with stochastic optimization. We consider
Externí odkaz:
http://arxiv.org/abs/2102.12586
We study the optimization problem for decomposing $d$ dimensional fourth-order Tensors with $k$ non-orthogonal components. We derive \textit{deterministic} conditions under which such a problem does not have spurious local minima. In particular, we s
Externí odkaz:
http://arxiv.org/abs/1911.09815
Publikováno v:
International Conference on Learning Representation, 2020
Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives. When these algorithms are only trained to minimize the training/test error, they could suffer from systematic
Externí odkaz:
http://arxiv.org/abs/1906.12005
The ubiquity of missing values in real-world datasets poses a challenge for statistical inference and can prevent similar datasets from being analyzed in the same study, precluding many existing datasets from being used for new analyses. While an ext
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9cdc12c91342cd5f414d66d1280f5c3
http://arxiv.org/abs/2109.00644
http://arxiv.org/abs/2109.00644
Autor:
Baharlouei S; University of Southern California., Ogudu K; University of Southern California., Suen SC; University of Southern California., Razaviyayn M; University of Southern California.
Publikováno v:
Transactions on machine learning research [Transact Mach Learn Res] 2023 Sep; Vol. 2023.
