arXiv:2509.13651v1 Announce Type: new Abstract: The fairness-accuracy trade-off is a key challenge in NLP tasks. Current work focuses on finding a single “optimal” solution to balance the two objectives, which is limited considering the diverse solutions on the Pareto front.…
arXiv:2509.13980v1 Announce Type: cross Abstract: In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level…
arXiv:2509.13686v1 Announce Type: new Abstract: Accurate localized wireless channel modeling is a cornerstone of cellular network optimization, enabling reliable prediction of network performance during parameter tuning. Localized statistical channel modeling (LSCM) is the state-of-the-art channel modeling framework tailored for cellular…
arXiv:2509.14032v1 Announce Type: cross Abstract: We study the existence and computation of Nash equilibria in continuous static games where the players’ admissible strategies are subject to shared coupling constraints, i.e., constraints that depend on their emph{joint} strategies. Specifically, we focus…
arXiv:2509.13717v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rigorous statistical guarantees. In this work, we bridge this gap by introducing…
arXiv:2509.14203v1 Announce Type: cross Abstract: Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. The average-reward formulation, while natural in many operations…
arXiv:2509.13725v1 Announce Type: new Abstract: Social anxiety is a common mental health condition linked to significant challenges in academic, social, and occupational functioning. A core feature is elevated momentary (state) anxiety in social situations, yet little prior work has measured…
arXiv:2404.17916v3 Announce Type: replace Abstract: Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this paper proposes a novel personalized federated…
arXiv:2509.13735v1 Announce Type: new Abstract: Directed graphs are ubiquitous across numerous domains, where the directionality of edges encodes critical causal dependencies. However, existing GNNs and graph Transformers tailored for directed graphs face two major challenges: (1) effectively capturing long-range causal…
arXiv:2501.06718v2 Announce Type: replace Abstract: Decision Transformer (DT), a trajectory modelling method, has shown competitive performance compared to traditional offline reinforcement learning (RL) approaches on various classic control tasks. However, it struggles to learn optimal policies from suboptimal, reward-labelled trajectories.…
