Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
arXiv:2602.12567v1 Announce Type: new Abstract: Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from...
Block-Sample MAC-Bayes Generalization Bounds
arXiv:2602.12605v1 Announce Type: new Abstract: We present a family of novel block-sample MAC-Bayes bounds (mean approximately correct). While PAC-Bayes bounds (probably approximately correct) typically give bounds for the generalization error that hold with high probability, MAC-Bayes bounds have a similar...
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
arXiv:2602.12613v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas many practical applications require continuous predictions, that predictions are issued frequently...
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
arXiv:2602.12624v1 Announce Type: new Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by high sampling costs. While prior work focuses on training objectives or individual solvers, the holistic design of...
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
arXiv:2602.12636v1 Announce Type: new Abstract: Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity severely limits RL sample efficiency. While...
SLA2: Sparse-Linear Attention with Learnable Routing and QAT
arXiv:2602.12675v1 Announce Type: new Abstract: Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a heuristic split that assigns computations to the sparse or...
Flow Matching from Viewpoint of Proximal Operators
arXiv:2602.12683v1 Announce Type: new Abstract: We reformulate Optimal Transport Conditional Flow Matching (OT-CFM), a class of dynamical generative models, showing that it admits an exact proximal formulation via an extended Brenier potential, without assuming that the target distribution has a...
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts - ACL Anthology
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations - ACL Anthology
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations - ACL Anthology
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts - ACL Anthology
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Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track - ACL Anthology
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing - ACL Anthology
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing - ACL Anthology
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track - ACL Anthology
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track - ACL Anthology
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations - ACL Anthology
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track - ACL Anthology
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