arXiv:2503.07680v3 Announce Type: replace Abstract: Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider…
arXiv:2510.09718v1 Announce Type: new Abstract: We consider the problem of federated clustering, where interconnected devices have access to private local datasets and need to jointly cluster the overall dataset without sharing their local dataset. Our focus is on hard clustering…
arXiv:2505.11740v2 Announce Type: replace Abstract: Fair classification is a critical challenge that has gained increasing importance due to international regulations and its growing use in high-stakes decision-making settings. Existing methods often rely on adversarial learning or distribution matching across sensitive…
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…
arXiv:2510.09664v1 Announce Type: new Abstract: Recently, deep supervised cross-modal hashing methods have achieve compelling success by learning semantic information in a self-supervised way. However, they still suffer from the key limitation that the multi-label semantic extraction process fail to explicitly…
