arXiv Machine Learning June 18, 2026 By Florian A. D. Burnat

QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization

Read the original on arXiv Machine Learning →

arXiv:2605. 04267v2 Announce Type: replace Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on eliciting decision-maker preferences that identify the relevant region of the Pareto set.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

1d ago

PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding

arXiv:2602. 08261v2 Announce Type: replace Abstract: Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Action (CPA).

By Binglin Wu, Yingyi Zhang, Xianneng Li, Ruyue Deng, Chuan Yue, Weiru Zhang, Xiaoyi Zeng

llms benchmarks

arXiv AI

Jun 9

Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries

arXiv:2606. 08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives.

By Linfeng Cao, Ming Shi, Ness B. Shroff

reinforcement-learning

arXiv AI

Jul 28

Efficiency Matters in Autonomous Research

arXiv:2607. 24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks.

By Haiqian Yang, Yuan Cao

agents reinforcement-learning

arXiv Machine Learning

Jul 31

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.

By Shengda Gu, Kai Li, Xinyi Ke, Haobo Fu, Yifan Zhang, Jian Cheng

llms reinforcement-learning

arXiv AI

Jul 7

Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds

arXiv:2506. 21887v2 Announce Type: replace Abstract: High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations.

By Edward Chen, Sang T. Truong, Natalie Dullerud, Sanmi Koyejo, Carlos Guestrin

arXiv Machine Learning

Jun 2

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

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

reinforcement-learning benchmarks

Related stories

PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding

Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries

Efficiency Matters in Autonomous Research

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA