This work introduces TSMOO (Thompson Sampling with Multi-Objective Optimization), a method that bridges the gap by combining multi-metric optimization with continuous learning in batch traffic allocation and outperforming both single-metric and elimination-based baselines.
This work proposes a model-based approach that optimizes model parameters, and evaluates first- and best-improvement algorithms, simulated annealing (SA), threshold accepting (TA), and two novel algorithms utilizing directional derivatives (dd) to guide exchanges.
JANUS is a plug-and-play infill module that extracts a local Jacobian from the recent evaluation trace, and gives the best mean cost on 1135-dimensional UAV path planning and improves SMS-EMOA/AGE-MOEA2 hosts on 12/38 multi-objective tasks with zero significant regressions.
Hongyuan Yu, Pufan Xu, Jiaojiao Yi et al.· 0 citations
Optimization of hyperparameters is a critical factor to obtain optimal model performance. While existing research has predominantly concentrated on batch-learning scenarios, addressing the complexities inherent in data streams presents a challenge. The deployment of sophisticated methodologies to manage data streams be...
This paper develops a differentiable framework for multi-objective MaxCut by combining an adjacency-based quadratic formulation with linear scalarization, thereby reducing the problem to a preference-conditioned single-objective signed-weight MaxCut problem.
Jing-Hang Huang, Alvaro Velasquez, Jia Liu et al.· 0 citations
This work proposes Multi-Marginal Preference Optimization (MMPO), a fine-grained framework that intervenes at the data, gradient, and constraint levels rather than relying on coarse-grained global scalarization to address optimization conflicts among multiple objectives in real-world deployment scenarios.
Shang-Wen Mao, Hao Zhang, Guangtao Nie et al.· 1 citation· ⚡1
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