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Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Thompson sampling (TS) is widely used in sequential decision making due to its ease of use and appealing empirical performance.
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Focusing on Gaussian inferential models (or variational approximating families) with diagonal plus low-rank precision matrices, we initiate a theoretical study of the trade-offs in two aspects, Bayesian posterior inference error and frequentist uncertainty quantification error.
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines
In a SelfLearning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated.
Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm
However, by systematically capturing and analyzing the feedback patterns of human users, vital information concerning the multimedia contents can be harvested for effective indexing and subsequent search.
Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation
Rewards and punishments in different forms are pervasive and present in a wide variety of decision-making scenarios.
Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective
A situation that often calls for learning termination is when the number of negative rewards exceeds the number of positive rewards.
Stochastic Reinforcement Learning
In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation.
