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Found 8 papers, 0 papers with code
Variational Neural Stochastic Differential Equations with Change Points
We develop two methodologies for modeling and estimating change points in time-series data with distribution shifts.
Constrained Reinforcement Learning Under Model Mismatch
Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment.
Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
Given a time series dataset, the proposed method jointly learns the unknown change points and the parameters of distinct neural SDE models corresponding to each change point.
Quickest Change Detection in Autoregressive Models
The data-driven setting where the disturbance signal parameters are unknown is further investigated, and an online and computationally efficient gradient ascent CuSum algorithm is designed.
Data-Driven Quickest Change Detection in (Hidden) Markov Models
The WADD is at most of the order of the logarithm of the ARL.
Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples
The goal of the fusion center is to detect the anomaly with minimal detection delay subject to false alarm constraints.
Kernel Robust Hypothesis Testing
For the Bayesian setting where the goal is to minimize the worst-case error probability, an optimal test is firstly obtained when the alphabet is finite.
Quickest Change Detection in Anonymous Heterogeneous Sensor Networks
The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied.
