It is shown that TabPFN v2 (Hollmann et al., 2025), a pretrained tabular foundation model never trained on Bayesian optimization data, can serve as a drop-in zero-shot BO surrogate, eliminating the per-task fitting step.
Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job, but it is found that it misestimates the cross-task correlation even in the simplest non-trivial case.
Carl Hvarfner, Sam Daulton, M. Balandat et al.· 0 citations
ReBRAC-v2 is introduced, which directly trains an exact-likelihood normalizing flow as the RL actor, combines likelihood, MSE, and MAE behavior regularization, and integrates a classification-based residual critic, staged optimization, and multi-sample test-time action selection, and ranks first in eight categories.
LAB-Tab is proposed, an LLM-augmented Bayesian network (BN) adaptation framework for source-aware few-shot tabular generation that achieves the best performance at the 10% target-data budget, leads four of the six individual scenarios, and reduces the macro Overall score by 33.8% relative to the strongest baseline.
Zijian Shen, Taijie Chen, Bin Zhou et al.· 0 citations
SnapViT: single-shot network approximation for pruned Vision Transformers is introduced, a new post-pretraining structured pruning method that enables elastic inference across a continuum of compute budgets, and a self-supervised importance scoring mechanism that maintains strong performance without requiring retrainin...
Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort et al.· 0 citations
This work runs the identical four-way comparison across six tasks spanning regression, generation, language modeling, reinforcement learning, and clinical and genomic classification, holding the specialist, the context, and (where the authors can) the training budget fixed.
A unified framework, KENDO (Kernel ENsemble Disagreement-aware Operator), is proposed that integrates Ensemble Gaussian Processes (EGP) with disagreement-aware acquisition strategies and extends the approach to multi-objective optimization via random scalarization that preserves the single-optimizer conditioning struct...
Heng Zhang, Hao-Tian Xiang, Konstantinos D. Polyzos et al.· 1 citation
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