Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Khalili, Mohammad Mahdi"'
Cardiovascular disease is a major life-threatening condition that is commonly monitored using electrocardiogram (ECG) signals. However, these signals are often contaminated by various types of noise at different intensities, significantly interfering
Externí odkaz:
http://arxiv.org/abs/2407.11065
Differential privacy mechanisms such as the Gaussian or Laplace mechanism have been widely used in data analytics for preserving individual privacy. However, they are mostly designed for continuous outputs and are unsuitable for scenarios where discr
Externí odkaz:
http://arxiv.org/abs/2406.02599
Machine learning systems have been widely used to make decisions about individuals who may behave strategically to receive favorable outcomes, e.g., they may genuinely improve the true labels or manipulate observable features directly to game the sys
Externí odkaz:
http://arxiv.org/abs/2405.01797
The use of machine learning models in high-stake applications (e.g., healthcare, lending, college admission) has raised growing concerns due to potential biases against protected social groups. Various fairness notions and methods have been proposed
Externí odkaz:
http://arxiv.org/abs/2311.05420
Supervised learning models have been used in various domains such as lending, college admission, face recognition, natural language processing, etc. However, they may inherit pre-existing biases from training data and exhibit discrimination against p
Externí odkaz:
http://arxiv.org/abs/2311.03714
Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist
Externí odkaz:
http://arxiv.org/abs/2310.06341
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a model of the model). Approximating the black-box with a metamodel can
Externí odkaz:
http://arxiv.org/abs/2302.04791
Autor:
Aminian, Gholamali, Abroshan, Mahed, Khalili, Mohammad Mahdi, Toni, Laura, Rodrigues, Miguel R. D.
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in many applications. In many scenarios, the data is collected sequentially
Externí odkaz:
http://arxiv.org/abs/2202.12123
We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given applicant using a pre-trained supervised learning model until all the
Externí odkaz:
http://arxiv.org/abs/2110.13986
Nowadays, rating systems play a crucial role in the attraction of customers for different services. However, as it is difficult to detect a fake rating, attackers can potentially impact the rating's aggregated score unfairly. This malicious behavior
Externí odkaz:
http://arxiv.org/abs/2101.10954