DT-PBO is a tree-based surrogate for preferential Bayesian optimization that matches GP performance on benchmarks while remaining inherently interpretable.
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Last-layer linearization for Bayesian GLMs in DNN uncertainty quantification matches full-network performance in UQ quality while improving efficiency, according to random matrix theory analysis and empirical tests across tasks.
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DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
DT-PBO is a tree-based surrogate for preferential Bayesian optimization that matches GP performance on benchmarks while remaining inherently interpretable.
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Is the Last Layer Sufficient for Uncertainty Quantification?
Last-layer linearization for Bayesian GLMs in DNN uncertainty quantification matches full-network performance in UQ quality while improving efficiency, according to random matrix theory analysis and empirical tests across tasks.