Tootfinder

On the role of network structure in learning to coordinate with bounded rationality
Yifei Zhang, Marcos M. Vasconcelos
https://arxiv.org/abs/2403.15683 htt…

On the role of network structure in learning to coordinate with bounded rationality
Many socioeconomic phenomena, such as technology adoption, collaborative problem-solving, and content engagement, involve a collection of agents coordinating to take a common action, aligning their decisions to maximize their individual goals. We consider a model for networked interactions where agents learn to coordinate their binary actions under a strict bound on their rationality. We first prove that our model is a potential game and that the optimal action profile is always to achieve perf…

Network Learning with Directional Sign Patterns
Anqi Dong, Can Chen, Tryphon T. Georgiou
https://arxiv.org/abs/2403.14915 https://arx…

Network Learning with Directional Sign Patterns
Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks.…

Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
Xiaofei Wang, Yunfeng Zhao, Chao Qiu, Qinghua Hu, Victor C. M. Leung
https://arxiv.org/abs/2404.13348

Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces soci…

Reconstructing the evolution history of networked complex systems
Junya Wang, Yi-Jiao Zhang, Cong Xu, Jiaze Li, Jiachen Sun, Jiarong Xie, Ling Feng, Tianshou Zhou, Yanqing Hu
https://arxiv.org/abs/2403.14983

Reconstructing the evolution history of networked complex systems
The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating stru…

This https://arxiv.org/abs/2306.14012 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Private Networked Federated Learning for Nonsmooth Objectives
This paper develops a networked federated learning algorithm to solve nonsmooth objective functions. To guarantee the confidentiality of the participants with respect to each other and potential eavesdroppers, we use the zero-concentrated differential privacy notion (zCDP). Privacy is achieved by perturbing the outcome of the computation at each client with a variance-decreasing Gaussian noise. ZCDP allows for better accuracy than the conventional $(ε, δ)$-DP and stronger guarantees than the …

Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning
Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas
https://arxiv.org/abs/2404.10842

Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning
This paper presents a computationally efficient and distributed speaker diarization framework for networked IoT-style audio devices. The work proposes a Federated Learning model which can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Federated Learning model which depends on cosine similarity of speaker embeddings. Moreover, the proposed diarization system solves the problem…

This https://arxiv.org/abs/2401.16183 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Scalable Reinforcement Learning for Linear-Quadratic Control of Networks
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algori…

Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning
Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas
https://arxiv.org/abs/2404.10842

Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning
This paper presents a computationally efficient and distributed speaker diarization framework for networked IoT-style audio devices. The work proposes a Federated Learning model which can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Federated Learning model which depends on cosine similarity of speaker embeddings. Moreover, the proposed diarization system solves the problem…

This https://arxiv.org/abs/2010.08311 has been replaced.
link: https://scholar.google.com/scholar?q=a

Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems
This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system. The LE-SESs are networked systems, composed of a set of connected components including: Bayes filters for state estimation, and neural ne…

This https://arxiv.org/abs/2401.16183 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Scalable Reinforcement Learning for Linear-Quadratic Control of Networks
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algori…

This https://arxiv.org/abs/2010.08311 has been replaced.
link: https://scholar.google.com/scholar?q=a

Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems
This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system. The LE-SESs are networked systems, composed of a set of connected components including: Bayes filters for state estimation, and neural ne…

This https://arxiv.org/abs/2306.02766 has been replaced.
link: https://scholar.google.com/scholar?q=a

Networked Communication for Decentralised Agents in Mean-Field Games
We introduce networked communication to the mean-field game framework, in particular to oracle-free settings where $N$ decentralised agents learn along a single, non-episodic evolution path of the empirical system. We prove that our architecture, with only a few reasonable assumptions about network structure, has sample guarantees bounded between those of the centralised- and independent-learning cases. We discuss how the sample guarantees of the three theoretical algorithms do not actually res…

Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Christian Nauck, Michael Lindner, Nora Molkenthin, J\"urgen Kurths, Eckehard Sch\"oll, J\"org Raisch, Frank Hellmann
https://arxiv.org/abs/2402.17500 …

Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
A central question of network science is how functional properties of systems arise from their structure. For networked dynamical systems, structure is typically quantified with network measures. A functional property that is of theoretical and practical interest for oscillatory systems is the stability of synchrony to localized perturbations. Recently, Graph Neural Networks (GNNs) have been shown to predict this stability successfully; at the same time, network measures have struggled to paint…

Scalable Reinforcement Learning for Linear-Quadratic Control of Networks
Johan Olsson, Runyu Zhang, Emma Tegling, Na Li
https://arXiv.org/abs/2401.16183 ht…

Scalable Reinforcement Learning for Linear-Quadratic Control of Networks
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algori…

This https://arxiv.org/abs/2403.14915 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Network Learning with Directional Sign Patterns
Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks.…

This https://arxiv.org/abs/2311.13967 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
This paper characterizes a new parametrization of nonlinear networked incrementally $L_2$-bounded operators in discrete time. The distinctive novelty is that our parametrization is \emph{free} -- that is, a sparse large-scale operator with bounded incremental $L_2$ gain is obtained for any choice of the real values of our parameters. This property allows one to freely search over optimal parameters via unconstrained gradient descent, enabling direct applications in large-scale optimal control a…

This https://arxiv.org/abs/2401.13947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Networked Multiagent Reinforcement Learning for Peer-to-Peer Energy Trading
Utilizing distributed renewable and energy storage resources in local distribution networks via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy systems' resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have the expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose mult…