IQS-BO: In-Context Query Selection for Bayesian Optimisation

arXiv:2610.01269 · cs.LG, stat.ML · Submitted 2026-10-01 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/automl/PFNs4BO2https:

Terminology

• Regret — 49× in this paper · explained in 3 episodes
• Acquisition Function — 26× in this paper · explained in 1 episode
• Transformer — 14× in this paper · explained in 2 episodes
• Bayesian Optimization — 10× in this paper · explained in 3 episodes
• Bayesian Optimisation — 9× in this paper
• Ablation — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• International Conference on Learning Representations — 4× in this paper
• AUC — 3× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 2× in this paper
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Meta-learning — 2× in this paper · explained in 2 episodes
• Prior-data Fitted Network — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
• TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
• Efficient Adaptive Data Acquisition via Pretrained Belief Representations
• TabICLv2: A better, faster, scalable, and open tabular foundation model
• Proximal Policy Optimization Algorithms

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