arXiv:2510.09705v1 Announce Type: new Abstract: Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection…
arXiv:2402.03941v3 Announce Type: replace Abstract: Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies on high-quality measured variables,…
arXiv:2510.09712v1 Announce Type: new Abstract: The spread of fake news online distorts public judgment and erodes trust in social media platforms. Although recent fake news detection (FND) models perform well in standard settings, they remain vulnerable to adversarial comments-authored by…
arXiv:2412.03611v4 Announce Type: replace Abstract: Estimating the frequency of items on the high-volume, fast data stream has been extensively studied in many areas, such as database and network measurement. Traditional sketches provide only coarse estimates under strict memory constraints. Although…
arXiv:2506.24000v2 Announce Type: replace Abstract: Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring additional labeled data. However, current TTA researches generally suffer from major limitations…
arXiv:2509.18171v2 Announce Type: replace Abstract: Federated Graph Learning (FGL) under domain skew — as observed on platforms such as emph{Twitch Gamers} and multilingual emph{Wikipedia} networks — drives client models toward incompatible representations, rendering naive aggregation both unstable and ineffective. We…
arXiv:2502.01755v4 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) optimize federated training by reducing computational and communication costs. We propose RoLoRA, a federated framework using alternating optimization to fine-tune LoRA adapters. Our approach emphasizes the importance…
arXiv:2505.17370v4 Announce Type: replace Abstract: Long-term time-series forecasting (LTSF) models are often presented as general-purpose solutions that can be applied across domains, implicitly assuming that all data is pointwise predictable. Using chaotic systems such as Lorenz-63 as a case study,…
arXiv:2510.11011v1 Announce Type: cross Abstract: Data prefetching–loading data into the cache before it is requested–is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning-based approaches, especially those leveraging data semantics, achieve…
arXiv:2510.11676v1 Announce Type: cross Abstract: We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex function whose subgradients are estimated under heavy-tailed noise. Existing work often employs gradient…
