EXPLORA: AI/ML EXPLainability for the Open RAN
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[1] A. Mahimkar,et al. Chroma: Learning and Using Network Contexts to Reinforce Performance Improving Configurations , 2023, MobiCom.
[2] Jingpu Duan,et al. TapFinger: Task Placement and Fine-Grained Resource Allocation for Edge Machine Learning , 2023, IEEE INFOCOM 2023 - IEEE Conference on Computer Communications.
[3] Fei Wang,et al. More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning , 2023, IEEE INFOCOM 2023 - IEEE Conference on Computer Communications.
[4] M. Fiore,et al. Spotting Deep Neural Network Vulnerabilities in Mobile Traffic Forecasting with an Explainable AI Lens , 2023, IEEE INFOCOM 2023 - IEEE Conference on Computer Communications.
[5] W. Willinger,et al. AI/ML for Network Security: The Emperor has no Clothes , 2022, CCS.
[6] Pedro Enrique Iturria Rivera,et al. Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN) , 2022, Sensors.
[7] M. Fiore,et al. Toward native explainable and robust AI in 6G networks: Current state, challenges and road ahead , 2022, Comput. Commun..
[8] Qingling Zhao,et al. LRP-based Policy Pruning and Distillation of Reinforcement Learning Agents for Embedded Systems , 2022, 2022 IEEE 25th International Symposium On Real-Time Distributed Computing (ISORC).
[9] Tianhang Zheng,et al. Poisoning Attacks on Deep Learning based Wireless Traffic Prediction , 2022, IEEE INFOCOM 2022 - IEEE Conference on Computer Communications.
[10] Pablo J. Rojo Maroni,et al. Automated identification of network anomalies and their causes with interpretable machine learning: The CIAN methodology and TTrees implementation , 2022, Comput. Commun..
[11] Elvina Gindullina,et al. Shapley Value as an Aid to Biomedical Machine Learning: a Heart Disease Dataset Analysis , 2022, 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid).
[12] D. Gao,et al. Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control , 2022, IEEE Transactions on Computational Social Systems.
[13] T. Lipić,et al. Explainability in reinforcement learning: perspective and position , 2022, ArXiv.
[14] E. Letouzé,et al. Trust, regulation, and human-in-the-loop AI , 2022, Commun. ACM.
[15] S. Basagni,et al. OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN , 2022, 2022 IEEE Wireless Communications and Networking Conference (WCNC).
[16] Tommaso Melodia,et al. ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control , 2022, IEEE INFOCOM 2022 - IEEE Conference on Computer Communications.
[17] Yuedong Xu,et al. Enabling Robust DRL-Driven Networking Systems via Teacher-Student Learning , 2022, IEEE Journal on Selected Areas in Communications.
[18] Stefano Basagni,et al. ColO-RAN: Developing Machine Learning-Based xApps for Open RAN Closed-Loop Control on Programmable Experimental Platforms , 2021, IEEE Transactions on Mobile Computing.
[19] X. Costa,et al. O-RAN: Disrupting the Virtualized RAN Ecosystem , 2021, IEEE Communications Standards Magazine.
[20] Marcin Dryjański,et al. Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps , 2021, Sensors.
[21] V. Ramaswamy,et al. An O-RAN Approach to Spectrum Sharing Between Commercial 5G and Government Satellite Systems , 2021, MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM).
[22] R. Piechocki,et al. RLOps: Development Life-Cycle of Reinforcement Learning Aided Open RAN , 2021, IEEE Access.
[23] Jacobus E. van der Merwe,et al. NexRAN: Closed-loop RAN slicing in POWDER -A top-to-bottom open-source open-RAN use case , 2021, WiNTECH@MOBICOM.
[24] S. Basagni,et al. Colosseum, the world's largest wireless network emulator , 2021, MobiCom.
[25] Fabien Couthouis,et al. Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning With Shapley Values , 2021, IEEE Computational Intelligence Magazine.
[26] Jiahai Yang,et al. DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications , 2021, CCS.
[27] Lei He,et al. Explainable Deep Reinforcement Learning for UAV autonomous path planning , 2021 .
[28] Peter Vamplew,et al. Explainable reinforcement learning for broad-XAI: a conceptual framework and survey , 2021, Neural Computing and Applications.
[29] Ajay Mahimkar,et al. Auric: using data-driven recommendation to automatically generate cellular configuration , 2021, SIGCOMM.
[30] Kun Kuang,et al. Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning , 2021, KDD.
[31] Osbert Bastani,et al. Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding , 2021, 2021 American Control Conference (ACC).
[32] Marco Canini,et al. Analyzing Learning-Based Networked Systems with Formal Verification , 2021, IEEE INFOCOM 2021 - IEEE Conference on Computer Communications.
[33] David A. Broniatowski. Psychological Foundations of Explainability and Interpretability in Artificial Intelligence , 2021 .
[34] Tuyen X. Tran,et al. Streaming From the Air : Enabling Drone-Sourced Video Streaming Applications on 5G Open-RAN Architectures , 2021, IEEE Transactions on Mobile Computing.
[35] T. Ho,et al. Joint Server Selection, Cooperative Offloading and Handover in Multi-Access Edge Computing Wireless Network: A Deep Reinforcement Learning Approach , 2020, IEEE Transactions on Mobile Computing.
[36] Antonios Tsourdos,et al. Trustworthy Deep Learning in 6G-Enabled Mass Autonomy: From Concept to Quality-of-Trust Key Performance Indicators , 2020, IEEE Vehicular Technology Magazine.
[37] Ying-Chang Liang,et al. Joint Optimization of Handover Control and Power Allocation Based on Multi-Agent Deep Reinforcement Learning , 2020, IEEE Transactions on Vehicular Technology.
[38] Weisi Guo. Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine , 2020, IEEE Communications Magazine.
[39] Eric M. S. P. Veith,et al. Explainable Reinforcement Learning: A Survey , 2020, CD-MAKE.
[40] Hugh Chen,et al. From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.
[41] Hongzi Mao,et al. Interpreting Deep Learning-Based Networking Systems , 2019, SIGCOMM.
[42] Marco Canini,et al. Cracking Open the Black Box: What Observations Can Tell Us About Reinforcement Learning Agents , 2019, NetAI@SIGCOMM.
[43] Michael Schapira,et al. Verifying Deep-RL-Driven Systems , 2019, NetAI@SIGCOMM.
[44] Marco Gramaglia,et al. vrAIn: A Deep Learning Approach Tailoring Computing and Radio Resources in Virtualized RANs , 2019, MobiCom.
[45] David W. Aha,et al. DARPA's Explainable Artificial Intelligence (XAI) Program , 2019, AI Mag..
[46] Tim Miller,et al. Explainable Reinforcement Learning Through a Causal Lens , 2019, AAAI.
[47] Tim Oates,et al. On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning , 2019, ACM Great Lakes Symposium on VLSI.
[48] Xin Jin,et al. Neural packet classification , 2019, SIGCOMM.
[49] Ying-Chang Liang,et al. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.
[50] Yuedong Xu,et al. Demystifying Deep Learning in Networking , 2018, APNet '18.
[51] Xia Hu,et al. Techniques for interpretable machine learning , 2018, Commun. ACM.
[52] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[53] Chi Harold Liu,et al. Experience-driven Networking: A Deep Reinforcement Learning based Approach , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
[54] Geoffrey Ye Li,et al. Hypergraph Theory: Applications in 5G Heterogeneous Ultra-Dense Networks , 2017, IEEE Communications Magazine.
[55] Ufuk Topcu,et al. Safe Reinforcement Learning via Shielding , 2017, AAAI.
[56] Hongzi Mao,et al. Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.
[57] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[58] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[59] Marco Tulio Ribeiro,et al. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier , 2016, NAACL.
[60] Jennifer Neville,et al. Attributed graph models: modeling network structure with correlated attributes , 2014, WWW.
[61] Martin A. Riedmiller,et al. Deep auto-encoder neural networks in reinforcement learning , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[62] M. Falkner,et al. A Survey on Intent-Based Networking , 2023, IEEE Communications Surveys & Tutorials.
[63] Theophilus A. Benson,et al. Configanator: A Data-driven Approach to Improving CDN Performance , 2022, NSDI.
[64] Malte Schwarzkopf,et al. How Reinforcement Learning Systems Fail and What to do About It , 2022 .
[65] Madhusanka Liyanage,et al. Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges , 2021, ArXiv.
[66] Florian Lorber,et al. Shield Synthesis for Reinforcement Learning , 2020, ISoLA.
[67] Klaus-Robert Müller,et al. Layer-Wise Relevance Propagation: An Overview , 2019, Explainable AI.