tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.
By L. A. Zhukov, E. V. Shaburova, D. V. Antonets
arXiv:2603. 29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R.
By Marc Becker, Lennart Schneider, Martin Binder, Lars Kotthoff, Bernd Bischl
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:2211. 14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement.
By Shuhei Watanabe, Frank Hutter
The paper introduces Gradient-based Sample Selection Bayesian Optimization (GSSBO), a method that builds the Gaussian process surrogate on a strategically chosen subset of samples rather than the full dataset. By using gradient information to eliminate redundant points while keeping diversity and representativeness, GSSBO achieves sublinear regret bounds and reduces the cubic computational cost of standard BO. Experiments on synthetic and real-world tasks show that this approach maintains comparable optimization performance while significantly cutting GP fitting time and resource usage.
By Qiyu Wei, Haowei Wang, Zirui Cao, Songhao Wang, Richard Allmendinger, Mauricio A \'Alvarez
arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.
By Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros, Grigorios M. Chatziathanasiou, Efi-Maria Papia, George Giannakopoulos
arXiv:2607. 27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive.
By Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet, Virginia Aglietti, Silvia Chiappa
arXiv:2605. 02427v3 Announce Type: replace Abstract: A recurring pattern in "reasoning without training" is that base LLMs already assign non-trivial probability mass to correct multi-step solutions; the bottleneck is locating these modes efficiently at inference time.
By Tu Nguyen, Matthieu Zimmer, Rasul Tutunov, Xiaotong Ji, Haitham Bou Ammar
arXiv:2607. 10669v1 Announce Type: new Abstract: Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective.
By L. A. Zhukov, E. V. Shaburova, D. V. Antonets
HyperMC is a multi‑fidelity hyperparameter tuning framework for stochastic gradient Markov chain Monte Carlo (SGMCMC) that combines Hyperband-style resource allocation with kernel Stein discrepancy (KSD) evaluation. It uses successive‑halving brackets to explore a continuous hyperparameter space while progressively refining promising configurations within a fixed computational budget. Robust HyperMC further introduces global grid initialization and elite‑guided local refinement to reduce sensitivity to random candidate generation and noisy evaluations, and theoretical analysis shows that the successive‑halving component selects a near‑optimal configuration with high probability under suitable conditions.
By Ming Tan, Xiyun Jiao
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
By Lukas Fehring, Marcel Wever, Maximilian Splieth\"over, Leona Hennig, Henning Wachsmuth, Marius Lindauer
arXiv:2607. 00259v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data.
By Afshar Shamsi, Xiao-Yu Guo, Hamid Alinejad-Rokny, Arash Mohammadi, Damien Teney, Ehsan Abbasnejad