The results for synthetic datasets show that when local features are present in data, the proposed method can outperform other feature selection methods. Furthermore, the results for microarray classification s
The method for sampling the actions is the key feature of the Monte-Carlo planning. Comparing to the traditional uniformed and randomized sampling methods, the multi-armed bandit algorithm has advantages in balancing the trade-offs between explorations and exploitations during the planning process, and...
We also propose an approach based on Kalman Filtering for Non-stationary Multi-armed Normal Bandits (KF-MANB) to leverage the coupling between models to learn more from each arm pull.We demonstrate that our method outperforms previous methods on synthetic trials, and performs competitively on ...
real world problems, including routing, QoS control, game playing, and resource allocation, can be solved in a decentralized manner when modeled as a system of interacting gambling machines.Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision ...
We compare HYPERBAND with state-of-the-art Bayesian Optimization methods on several hyperparameter optimization problems. We observe that HYPERBAND can provide over an order of magnitude speedups over competitors on a variety of neural network and kernel-based learning problems.Uation For...
They also suggested approximation methods for the implementation of the sliding window over time to reduce its computational cost. In [32], the authors proposed to use RSSI and ACK/negative ACK (NACK) signals to detect the changes of the channel status. Then, the results were used for a D...
Algorithms developed for solving RL problems, such as policy gradient methods, can be adapted for contextual bandit problems [16]. Currently, many efforts have been devoted to dealing with sequential decision-making problems using bandit-based approaches, where the decision maker seeks to select the...
Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make ...
Methods and analysis The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (...
The method for sampling the actions is the key feature of the Monte-Carlo planning. Comparing to the traditional uniformed and randomized sampling methods, the multi-armed bandit algorithm has advantages in balancing the trade-offs between explorations and exploitations during the planning process, and...