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A Meta-Learning Approach to Bayesian Causal Discovery

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Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?

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Continuous Bayesian Model Selection for Multivariate Causal Discovery

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Rethinking Aleatoric and Epistemic Uncertainty

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Turbulence: Systematically and automatically testing instruction-tuned large language models for code

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Bivariate Causal Discovery using Bayesian Model Selection

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Learning in Deep Factor Graphs with Gaussian Belief Propagation

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Noether' s Razor: Learning Conserved Quantities

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Transition Constrained Bayesian Optimization via Markov Decision Processes

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Actually Sparse Variational Gaussian Processes

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