Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
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[1] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[2] Arnak S. Dalalyan,et al. User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient , 2017, Stochastic Processes and their Applications.
[3] David Ginsbourger,et al. On the choice of the low-dimensional domain for global optimization via random embeddings , 2017, Journal of Global Optimization.
[4] Shuguang Cui,et al. Optimized Power Control for Over-the-Air Federated Edge Learning , 2020, ICC 2021 - IEEE International Conference on Communications.
[5] Matthias Poloczek,et al. A Framework for Bayesian Optimization in Embedded Subspaces , 2019, ICML.
[6] Osvaldo Simeone,et al. Privacy for Free: Wireless Federated Learning via Uncoded Transmission With Adaptive Power Control , 2020, IEEE Journal on Selected Areas in Communications.
[7] Jorge Nocedal,et al. Optimization Methods for Large-Scale Machine Learning , 2016, SIAM Rev..
[8] I. Johnstone. High dimensional Bernstein-von Mises: simple examples. , 2010, Institute of Mathematical Statistics collections.
[9] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[10] Petar Popovski,et al. Capacity of Remote Classification Over Wireless Channels , 2020, IEEE Transactions on Communications.
[11] Kaibin Huang,et al. Broadband Analog Aggregation for Low-Latency Federated Edge Learning , 2018, IEEE Transactions on Wireless Communications.
[12] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[13] Elchanan Mossel,et al. The Computational Complexity of Estimating MCMC Convergence Time , 2011, APPROX-RANDOM.
[14] Riccardo Moriconi,et al. High-dimensional Bayesian optimization using low-dimensional feature spaces , 2019, Machine Learning.
[15] Randy H. Katz,et al. A Berkeley View of Systems Challenges for AI , 2017, ArXiv.
[16] Osvaldo Simeone,et al. A Brief Introduction to Machine Learning for Engineers , 2017, Found. Trends Signal Process..
[17] Deniz Gündüz,et al. Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).
[18] Michael I. Jordan,et al. Variational Consensus Monte Carlo , 2015, NIPS.
[19] Faramarz Fekri,et al. Analog Compression and Communication for Federated Learning over Wireless MAC , 2020, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
[20] Faramarz Fekri,et al. Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning , 2020, AAAI.
[21] Arnaud Doucet,et al. An Adaptive Subsampling Approach for MCMC Inference in Large Datasets , 2014 .
[22] Chong Wang,et al. Asymptotically Exact, Embarrassingly Parallel MCMC , 2013, UAI.
[23] Mohak Shah,et al. On-Device Machine Learning: An Algorithms and Learning Theory Perspective , 2019, ArXiv.
[24] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[25] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[26] Joachim M. Buhmann,et al. Variational Federated Multi-Task Learning , 2019, ArXiv.
[27] Yonina C. Eldar,et al. Over-the-Air Federated Learning From Heterogeneous Data , 2020, IEEE Transactions on Signal Processing.
[28] Zhi Ding,et al. Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.
[29] Osvaldo Simeone,et al. Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis , 2021, IEEE Journal on Selected Areas in Communications.
[30] Babak Shahbaba,et al. Distributed Stochastic Gradient MCMC , 2014, ICML.
[31] Deniz Gündüz,et al. One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis , 2020, IEEE Transactions on Wireless Communications.
[32] Shuguang Cui,et al. Over-the-Air Computing for Wireless Data Aggregation in Massive IoT , 2020 .
[33] H. Vincent Poor,et al. Scheduling Policies for Federated Learning in Wireless Networks , 2019, IEEE Transactions on Communications.
[34] Kaibin Huang,et al. Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks , 2018, IEEE Transactions on Wireless Communications.
[35] Deniz Gündüz,et al. Blind Federated Edge Learning , 2020, IEEE Transactions on Wireless Communications.
[36] Kaibin Huang,et al. Towards an Intelligent Edge: Wireless Communication Meets Machine Learning , 2018, ArXiv.
[37] Osvaldo Simeone,et al. Free Energy Minimization: A Unified Framework for Modeling, Inference, Learning, and Optimization [Lecture Notes] , 2021, IEEE Signal Processing Magazine.
[38] Marios Kountouris,et al. Wireless Distributed Edge Learning: How Many Edge Devices Do We Need? , 2020, IEEE Journal on Selected Areas in Communications.
[39] R. Zamir,et al. Lattice Coding for Signals and Networks: A Structured Coding Approach to Quantization, Modulation and Multiuser Information Theory , 2014 .
[40] Alexander J. Smola,et al. Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.
[41] Eryk Dutkiewicz,et al. Optimal Online Data Partitioning for Geo-Distributed Machine Learning in Edge of Wireless Networks , 2019, IEEE Journal on Selected Areas in Communications.
[42] Kaibin Huang,et al. MIMO Over-the-Air Computation for High-Mobility Multimodal Sensing , 2018, IEEE Internet of Things Journal.
[43] Ryan P. Adams,et al. Patterns of Scalable Bayesian Inference , 2016, Found. Trends Mach. Learn..
[44] Syed A. Jafar,et al. Interference Alignment: A New Look at Signal Dimensions in a Communication Network , 2011, Found. Trends Commun. Inf. Theory.
[45] Hamed Haddadi,et al. Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.
[46] Edward I. George,et al. Bayes and big data: the consensus Monte Carlo algorithm , 2016, Big Data and Information Theory.
[47] Matus Telgarsky,et al. Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis , 2017, COLT.
[48] Osvaldo Simeone,et al. Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning, and Optimization , 2020, ArXiv.
[50] Meixia Tao,et al. Gradient Statistics Aware Power Control for Over-the-Air Federated Learning in Fading Channels , 2020, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).
[51] David Barber,et al. Bayesian reasoning and machine learning , 2012 .
[52] Dongning Guo,et al. Scheduling for Cellular Federated Edge Learning With Importance and Channel Awareness , 2020, IEEE Transactions on Wireless Communications.
[53] Mohamed-Slim Alouini,et al. Wireless Data Center Networks: Advances, Challenges, and Opportunities , 2018, ArXiv.
[54] S. Chib,et al. Bayesian analysis of binary and polychotomous response data , 1993 .
[55] Torsten Hoefler,et al. Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. , 2018 .
[56] Agustinus Kristiadi,et al. Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks , 2020, ICML.