Bayesian Model Merging introduces a bi-level optimization framework that merges task-specific models via closed-form Bayesian regression with an anchor prior and global hyperparameter search, outperforming baselines and nearly matching expert averages on up to 20-task vision and 5-task language Merg
Bayesian optimization
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Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with competitive or superior results on large-scale problems.
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Bayesian Model Merging
Bayesian Model Merging introduces a bi-level optimization framework that merges task-specific models via closed-form Bayesian regression with an anchor prior and global hyperparameter search, outperforming baselines and nearly matching expert averages on up to 20-task vision and 5-task language Merg
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Stein Variational Black-Box Combinatorial Optimization
Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with competitive or superior results on large-scale problems.