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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Sparse Deep Learning for Time Series Data: Theory and Applications
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Specifically, we lay the foundation within the Bayesian framework.
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Transformers Represent Belief State Geometry in their Residual Stream
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In Section 3, we verify that this geometry is linearly represented in the residual stream of transformers.
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Task-aware world model learning with meta weighting via bi-level optimization
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Aligning the world model with the environment for the agent's specific task is
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
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Further, we emphasize the significance of principled regularization of the network parameters and prediction.
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IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL *Pascal Leroy
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In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.
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On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
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We compute the convergence rates of the RSVB approximate posterior and the corresponding optimal value.
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