Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment Ben Usman
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The variability of what "similar" means in this context, which transformations are allowed, and whether data points themselves or their feature representations
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Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
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A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises.
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Contribution I: General Bayesian Framework
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However, one major contribution is that Table 1 can be extended with more columns (e.g. one more
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