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Found 9 papers, 0 papers with code
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling
Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends.
Continual Learning as Computationally Constrained Reinforcement Learning
The design of such agents, which remains a long-standing challenge of artificial intelligence, is addressed by the subject of continual learning.
A Semi-Supervised Framework for Misinformation Detection
The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest.
A Definition of Non-Stationary Bandits
Despite the subject of non-stationary bandit learning having attracted much recent attention, we have yet to identify a formal definition of non-stationarity that can consistently distinguish non-stationary bandits from stationary ones.
Brain Model State Space Reconstruction Using an LSTM Neural Network
Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters.
Non-Stationary Bandit Learning via Predictive Sampling
We attribute such failures to the fact that, when exploring, the algorithm does not differentiate actions based on how quickly the information acquired loses its usefulness due to non-stationarity.
Gaussian Imagination in Bandit Learning
We study the performance of an agent that attains a bounded information ratio with respect to a bandit environment with a Gaussian prior distribution and a Gaussian likelihood function when applied instead to a Bernoulli bandit.
Robust Convergence in Federated Learning through Label-wise Clustering
Non-IID dataset and heterogeneous environment of the local clients are regarded as a major issue in Federated Learning (FL), causing a downturn in the convergence without achieving satisfactory performance.
A Black-box NLP Classifier Attacker
Deep neural networks have a wide range of applications in solving various real-world tasks and have achieved satisfactory results, in domains such as computer vision, image classification, and natural language processing.
