The paper explores how to use gradients of simulator outputs with respect to input parameters to improve Bayesian inference for expensive simulators. By incorporating gradient information into Gaussian process surrogate models, the authors show that Bayesian optimization can converge faster under a limited simulation budget. They find that reverse‑mode differentiation yields efficiency gains, while forward‑mode does not offset its computational cost, especially when the number of parameters exceeds the output dimensionality.
By \v{S}imon Sold\'at, V\'aclav \v{S}m\'idl
IQS-BO introduces an in‑context query selection method for Bayesian optimisation that learns to choose evaluation points via supervised learning on synthetic priors. The approach uses a Prior‑data Fitted Network (PFN) to predict, in a single forward pass, the probability that each candidate maximises the objective, thereby amortising the decision step and eliminating the need for costly surrogate refitting or acquisition maximisation. Experiments show that IQS‑BO matches or surpasses traditional Gaussian‑process‑based BO and other in‑context methods on both synthetic and real‑world benchmarks, and that a mixture prior combining GP samples with challenging function types further improves performance.
By Luca Geminiani, Nadja Klein
arXiv:2610.08969v1 Announce Type: new
Abstract: Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency...
By Gustavo Sutter, Alejandro Comas-Leon, David Holzm\"uller, Hao Wang, Luis Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi
Batch black-box optimization is important for expensive systems where many candidates can be evaluated in parallel, yet only a few rounds of evaluation are affordable. In this regime, effective batch...
In-Context Batch Black-box Optimization via Flow Matching introduces FMTR, an amortized method that learns a continuous, history-conditioned proposal distribution for variable-size batch sampling in expensive black-box optimization settings. FMTR is trained offline using conditional flow matching on Thompson-sampling argmaxes refined through a horizon-dependent Langevin pushforward map, enabling adaptive exploration-exploitation over the optimization horizon. At test time, FMTR generates batches without task-specific surrogate refitting or numerical acquisition optimization, achieving strong performance on synthetic and real-world benchmarks while significantly reducing batch selection computational cost.
By Quoc-Anh Hoang Nguyen, Xinyu Zhang, Daolang Huang, Julien Martinelli, Samuel Kaski
arXiv:2607. 14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing.
By Mustafa Emre G\"ursoy, Stefan Uhlich, Ryoga Matsuo, Ya\u{g}{\i}z Gen\c{c}er, Arun Venkitaraman, Chia-Yu Hsieh, Andrea Bonetti, Eisaku Ohbuchi, Lorenzo Servadei
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
arXiv:2609.01493v1 Announce Type: cross
Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-w...
By Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue
arXiv:2507.06529v2 Announce Type: replace
Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate h...
By Fengxue Zhang, Yuxin Chen
arXiv:2610.10186v1 Announce Type: new
Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters...
By Satoshi Katayama, Shoyo Hunt, Shintaro Masuda, Masayuki Karasuyama
arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.
By Trevor Campbell, Jonathan H. Huggins, Kyurae Kim, Charles C. Margossian
arXiv:2607. 22238v1 Announce Type: new Abstract: Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation.
By Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa, Yusuke Hiki, Takashi Morikura, Akira Funahashi