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New research advances Bayesian optimization techniques · 2 sources tracked

By PulseAugur Editorial · [6 sources] ·

Two new research papers explore advancements in Bayesian optimization (BO), a technique for optimizing complex functions. The first paper introduces a direct regret optimization approach that jointly learns the model and acquisition function, outperforming standard baselines, especially in high-dimensional settings. The second paper delves into the theoretical understanding of time-varying Bayesian optimization (TVBO), providing bounds and conditions for achieving asymptotic no-regret performance, covering various kernel functions. AI

IMPACT Advances theoretical understanding and practical performance of optimization techniques crucial for AI model training and hyperparameter tuning.

RANK_REASON Two academic papers published on arXiv detailing new theoretical and practical approaches to Bayesian optimization.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New research advances Bayesian optimization techniques · 2 sources tracked

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COVERAGE [6]

  1. arXiv cs.LG TIER_1 English(EN) · Gustavo Sutter, Alejandro Comas-Leon, David Holzm\"uller, Hao Wang, Luis Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi ·

    Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization

    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 has two consequences during optimization: (i) the objective evaluation dominates…

  2. arXiv cs.LG TIER_1 English(EN) · Rikuto Matsumoto, Masanori Ishikura, Masayuki Karasuyama ·

    A Unified Information-Theoretic Approach to Constrained Multi-Fidelity Multi-Objective Bayesian Optimization

    arXiv:2610.10174v1 Announce Type: new Abstract: Bayesian optimization often involves multiple objectives, constraints, and fidelity levels. We address the challenge of jointly selecting where and at which fidelity to evaluate to identify the highest-fidelity feasible Pareto front…

  3. arXiv cs.LG TIER_1 English(EN) · Satoshi Katayama, Shoyo Hunt, Shintaro Masuda, Masayuki Karasuyama ·

    Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization

    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 that must be specified in advance, and their performance can strongly depend on t…

  4. arXiv cs.LG TIER_1 English(EN) · Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy ·

    BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability

    arXiv:2602.07144v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters being tuned come with a carefully engineered default configuration, and practitione…

  5. arXiv cs.LG TIER_1 English(EN) · Fengxue Zhang, Yuxin Chen ·

    Direct Regret Optimization in Bayesian Optimization

    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 hand-crafted acquisition functions and surrogate models for the underlying functio…

  6. arXiv cs.LG TIER_1 English(EN) · Anthony Bardou, Patrick Thiran ·

    Asymptotic Performance of Time-Varying Bayesian Optimization

    arXiv:2505.13012v3 Announce Type: replace-cross Abstract: Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying black-box objective function that may be noisy and expensive to evaluate, but its excellent empirical performance remains to be…

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