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Found 7 papers, 3 papers with code
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions
Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy.
Asymmetric Hashing for Fast Ranking via Neural Network Measures
Previous learning-to-hash approaches are also not suitable to solve the fast item ranking problem since they can take a significant amount of time and computation to train the hash functions.
Backdoor Attack with Imperceptible Input and Latent Modification
Many existing countermeasures found that backdoor tends to leave tangible footprints in the latent or feature space, which can be utilized to mitigate backdoor attacks. In this paper, we extend the concept of imperceptible backdoor from the input space to the latent representation, which significantly improves the effectiveness against the existing defense mechanisms, especially those relying on the distinguishability between clean inputs and backdoor inputs in latent space.
LIRA: Learnable, Imperceptible and Robust Backdoor Attacks
Under this optimization framework, the trigger generator function will learn to manipulate the input with imperceptible noise to preserve the model performance on the clean data and maximize the attack success rate on the poisoned data.
Regression via Implicit Models and Optimal Transport Cost Minimization
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x, z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$.
Gradient Boosting Neural Networks: GrowNet
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''.
Targeted display advertising: the case of preferential attachment
We present an experimental analysis on the historical logs of a major display advertising platform (https://www. criteo. com/).
