15 accepted papers
The study of enhancing model robustness against adversarial examples has become increasingly critical in the security of deep learning, leading to the development of numerous adversarial defense techniques. While these defense methods have shown promise in mitigating the impact of adversarial pertur…
Fast Adversarial Training (FAT) employs the single-step Fast Gradient Sign Method (FGSM) to generate adversarial examples, reducing the computational costs of traditional adversarial training. However, FAT suffers from Catastrophic Overfitting (CO), where models' robust accuracy against multi-step a…
Traditional adversarial training, while effective at improving machine learning model robustness, is computationally intensive. Fast Adversarial Training (FAT) addresses this by using a single-step attack to generate adversarial examples more efficiently. Nonetheless, FAT is susceptible to a phenome…
This paper is devoted to the problem of noise covariance matrix (NCM) estimation. It proposes a time-frequency masking based approach. We first present an optimal mask function based on the mean-squared error criterion. To estimate this mask, we employ the recently developed directional gain method…
The performance of Transfer Learning (TL) significantly depends on effective pretraining, which not only requires extensive amounts of data but also substantial computational resources. As a result, in practice, it is challenging to successfully perform TL at the level of individual model developers…
Graph Neural Networks (GNNs) have proven to be highly effective in solving real-world learning problems that involve graph-structured data. However, GNNs can also inadvertently expose sensitive user information and interactions through their model predictions. To address these privacy concerns, Diff…
With the adoption of recent laws ensuring the ``right to be forgotten'', the problem of machine unlearning has become of significant importance. This is particularly the case for graph-structured data, and learning tools specialized for such data, including graph neural networks (GNNs). This work in…
Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has beco…
Hypergraphs are used to model higher-order interactions amongst agents and there exist many practically relevant instances of hypergraph datasets. To enable the efficient processing of hypergraph data, several hypergraph neural network platforms have been proposed for learning hypergraph properties…
This paper is devoted to the problem of adaptive beamforming with small-spaced microphone arrays. In this context, the Wiener filter is an optimal beamformer in the mean-squared error (MSE) sense. However, it requires good estimates of the covariance matrices of the speech signal of interest and noi…
Microphone arrays have been used in wide range of applications for sound acquisition and signal enhancement, the performance of which depends not only on the processing algorithms but also on the array geometry. A large number of efforts have been devoted to the development of beamforming and signal…
Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theor…
ABSTRACT The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis. In particular, synthetic DNA products contain both individual oligo (fragment) symbol errors as we…
We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers. Our solution shows that, under some mild assumptions on the smallest cluster size, one can obtain an $(1+\epsilon)$-approximation for the optima…
The additive model is one of the most popularly used models for high dimensional nonparametric regression analysis. However, its main drawback is that it neglects possible interactions between predictor variables. In this paper, we reexamine the group additive model proposed in the literature, and r…