Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly. The effectiveness in reducing communication thus heavily depends on the quality of the dynamics models used …
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20 results for “event-triggered”
ET-GP-UCB optimizes time-varying functions without knowing change rates.
problem Sequentially optimizing a time-varying objective function with unknown change rates.
method Event-triggered Bayesian optimization with adaptive resets based on probabilistic uniform error bounds.
result ET-GP-UCB outperforms other GP-UCB algorithms in synthetic and real-world data.
Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
problem Reduce communication frequency in networked AP systems while maintaining control performance.
method Develops a DRL-based controller that avoids explicit update timing learning, using a semi-Markov decision process (SMDP).
result Improves communication efficiency without sacrificing control performance.
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimizationstat.ML
In this paper, we propose and analyze SPARQ-SGD, which is an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models. Each node can locally compute a condition (event) which triggers a communication where quantized and sparsified local model parameters are sent. In SPA…
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attackeess.SY
In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required comm…
We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby modeled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM algorithm …
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wi…
Federated Q-Learning achieves linear regret speedup with low communication cost.
problem Achieving linear regret speedup in federated reinforcement learning without high communication costs.
method Proposed two federated Q-Learning algorithms: FedQ-Hoeffding and FedQ-Bernstein, using event-triggered synchronization, novel step size selection, and concentration inequalities.
result Total regrets achieve linear speedup compared to single-agent counterparts with logarithmic communication cost.
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensionsstat.ML
This paper models how features influence event triggers in high-dimensional networks.
problem Estimating context-dependent networks in high-dimensional marked point processes.
method Leveraging compositional time series and regularization methods, the paper considers autoregressive multinomial and logistic-normal models for network estimation.
result The logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories.
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Datacs.LG
EVARS-GPR refines Gaussian Process Regression for seasonal data with sudden scale changes.
problem Challenges in forecasting with changing system behavior over time.
method Combines online change point detection with data augmentation for refitting.
result 20.8% lower RMSE on real-world datasets compared to similar methods.
Paper develops a neural model to assess cascading extreme events.
problem Risk assessment of domino effects like earthquakes and tsunamis.
method Develops a Kolmogorov-Arnold neural network (KANE) framework.
result Estimates the probability of one extreme event triggering another.
Computing large deviation prefactors of stochastic dynamical systems based on machine learningstat.ML
The paper uses machine learning to compute rare event probabilities in stochastic systems.
problem Characterizing rare events in stochastic dynamical systems with weak noise.
method Developed a neural network framework for computing quasipotential, most probable paths, and prefactors.
result Demonstrated higher effectiveness and accuracy of the algorithm in calculating mean exit times.
This review assesses deep-learning methods for complex sequential data.
problem Lack of robustness and transparency in deep-learning frameworks for irregular sequential data.
method Systematic literature review of existing algorithms.
result Recurrent neural networks dominate in performance evaluation of deep-learning frameworks.
Unifies 18 definitions of surprise, classifies them into four categories.
problem Lack of consensus on surprise definition.
method Technical classification into three groups based on agent's belief; conceptual categorization into four types.
result Taxonomy of surprise definitions provides foundation for brain studies.
A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systemsmath.DS
Develops a machine learning framework for computing most probable paths in stochastic systems.
problem Computing the most probable paths in stochastic dynamical systems.
method Reformulates the boundary value problem of Hamiltonian systems and uses a neural network to solve the Euler-Lagrange equation for the Onsager-Machlup action functional.
result Demonstrates the efficacy and accuracy of the machine learning approach in computing most probable paths for stochastic systems with various types of noise.
Spiking neural networks (SNNs) have garnered a great amount of interest for supervised and unsupervised learning applications. This paper deals with the problem of training multi-layer feedforward SNNs. The non-linear integrate-and-fire dynamics employed by spiking neurons make it difficult to train SNNs to generate de…
In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm and retains the advantages of Monte Carlo methods including low bias, simplicity, and ease of implementatio…
A Data-Driven Approach for Discovering Stochastic Dynamical Systems with Non-Gaussian Levy Noisestat.ML
Develops a new method to discover stochastic systems with non-Gaussian noise.
problem Discovering governing laws from complex systems with non-Gaussian noise.
method Theoretical framework and numerical algorithm to extract stochastic differential equations with Gaussian and non-Gaussian noise.
result Demonstrated the efficacy and accuracy of the approach on various systems.
A new method extracts events and their arguments efficiently from text.
problem Efficiently extract event information from texts with long-range dependencies and associations.
method Graph Convolutional Networks with shortest dependency paths to capture syntactic relationships.
result Significant improvement over state-of-the-art methods.
We present a careful analysis of possible issues on the application of the self-excited Hawkes process to high-frequency financial data. We carefully analyze a set of effects leading to significant biases in the estimation of the "criticality index" n that quantifies the degree of endogeneity of how much past events tr…