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Fairness-Aware Learning

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Demographics

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Disparities

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Fairness Constraints

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Fairness Audits

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Fairness Interventions

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243 papers

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Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networks
May 1, 2026Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen +1Wifi Fairness Interventions
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A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
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From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes
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Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
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Private Rate-Constrained Optimization with Applications to Fair Learning
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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation
May 21, 2025Kun Fang, Qinghua Tao, Mingzhen He +6Out-Of-Distribution Detection In-Distribution
Causally Fair Node Classification on Non-IID Graph Data
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FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization
Mar 29, 2025Yucong Dai, Jie Ji, Xiaolong Ma +1Fairness-Aware Learning Image Classification
SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence
Dec 6, 2024Bowei Tian, Yexiao He, Ziyao Wang +3Fairness-Aware Learning Counterfactual Learning
Which Demographics do LLMs Default to During Annotation?
Oct 11, 2024Johannes Schäfer, Aidan Combs, Christopher Bagdon +9Large Language Model Bias Demographics
When fairness metrics fail: A utility-based perspective on $\varepsilon$ -fairness
May 15, 2024Tolulope Fadina, Thorsten SchmidtFairness Constraints Expected Utility
Perturbation Effects on Robustness and Individual Fairness
Apr 1, 2024Xuran Li, Hao Xue, Peng Wu +4Fairness-Aware Learning Perturbations
A Distributionally Robust Optimisation Approach to Fair Credit Scoring
Feb 2, 2024Pablo Casas, Huan Yu, Christophe MuesFairness-Aware Learning Credit Risk Prediction

May 1, 2026 · cs.NIJ/K move · Enter open · S save

Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networks

Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen, Chiapin Wang

Research Center for Information Technology Innovation (CITI), Academia Sinica (AS), Taipei 11529, Taiwan · Department of Electrical Engineering, National Taiwan Normal University (NTNU), Taipei 10610 Taiwan

The coexistence of NR-U and Wi-Fi in the unlicensed spectrum introduces a challenging resource management problem, where heterogeneous channel access mechanisms can lead to unbalanced spectrum utilization and severe Wi-Fi performance degradation. To address this issue, this paper proposes a utility-aware deep reinforcement learning (DRL) framework for adaptive transmission opportunity (TXOP) control in NR-U/Wi-Fi coexistence networks. The coexistence process is formulated as a Markov decision process (MDP), in which the NR-U TXOP duration is treated as a controllable variable for regulating post-access channel occupancy. A deep Q-network (DQN) is then employed to learn adaptive TXOP control policies through online interaction with the coexistence environment. A key feature of the proposed framework is the integration of a configurable reward and criterion design, which enables explicit control of the fairness-efficiency-utility tradeoff. Three operating policies are developed, namely absolute fairness, moderate fairness, and utility-oriented moderate fairness, to characterize different coexistence operating points. Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared with the absolute fairness policy, the moderate fairness policy improves aggregate throughput by 68.22%, while the utility-oriented policy achieves a 177.6% improvement under the adopted utility evaluation metric. These results demonstrate that the proposed utility-aware DRL framework provides an effective and flexible solution for adaptive TXOP control and tradeoff management in heterogeneous unlicensed coexistence networks.

Wifi Fairness Interventions

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