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OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack

arXiv:2408.00329v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are vulnerable to small adversarial perturbations of the inputs, posing a significant challenge to their reliability and robustness. Empirical methods such as adversarial training can defend against particular attacks but remain…

Window-based Membership Inference Attacks Against Fine-tuned Large Language Models

arXiv:2601.02751v2 Announce Type: replace-cross Abstract: Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes the subtle, localized signals of memorization, reducing attack effectiveness. We…

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data

arXiv:2505.00940v3 Announce Type: replace Abstract: When synthesizing multi-source high-dimensional data, a key objective is to extract low-dimensional representations that effectively approximate the original features across different sources. Such representations facilitate the discovery of transferable structures and help mitigate systematic biases…

FlowTouch: View-Invariant Visuo-Tactile Prediction

arXiv:2603.08255v1 Announce Type: cross Abstract: Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings…

A Lightweight MPC Bidding Framework for Brand Auction Ads

arXiv:2603.07721v1 Announce Type: cross Abstract: Brand advertising plays a critical role in building long-term consumer awareness and loyalty, making it a key objective for advertisers across digital platforms. Although real-time bidding has been extensively studied, there is limited literature on…