{"type":"ScholarlyArticle","title":"Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling","authors":[{"name":"Tingyang Wei","slug":"tingyang-wei","given":"Tingyang","family":"Wei","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Jiao Liu","slug":"jiao-liu","given":"Jiao","family":"Liu","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Abhishek Gupta","slug":"abhishek-gupta","given":"Abhishek","family":"Gupta","affiliation":"School of Mechanical Sciences , Indian Institute of Technology , Goa , India"},{"name":"Chin Chun Ooi","slug":"chin-chun-ooi","given":"Chin Chun","family":"Ooi","affiliation":"Centre for Frontier AI Research , Agency for Science, Technology and Research , Singapore"},{"name":"Puay Siew Tan","slug":"puay-siew-tan","given":"Puay Siew","family":"Tan","affiliation":"Singapore Institute of Manufacturing Technology , Agency for Science, Technology and Research , Singapore"},{"name":"Yew-Soon Ong","slug":"yew-soon-ong","given":"Yew-Soon","family":"Ong","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore; Centre for Frontier AI Research , Agency for Science, Technology and Research , Singapore"}],"abstract":"Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as parametric expensive multi-objective optimization problems (P-EMOPs) where each task parameter defines a distinct optimization instance. Current multi-objective Bayesian optimization methods have been widely used for finding finite sets of Pareto optimal solutions for each task. However, P-EMOPs present a fundamental challenge: the continuous task parameter space can contain infinite distinct problems, each requiring separate expensive evaluations. To address this, we propose learning an inverse model to amortize the multi-objective optimization cost across the continuous task-preference space, enabling direct solution prediction for any query without the need for expensive re-evaluation. This paper introduces a novel parametric multi-objective Bayesian optimizer that learns this inverse model by alternating between (1) generative solution sampling via conditional generative models and (2) acquisition-driven search leveraging inter-task synergies. This approach enables effective optimization across multiple tasks and finally achieves direct solution prediction for unseen parameterized EMOPs without re-evaluations. We theoretically justify the faster convergence by leveraging inter-task synergies through task-aware Gaussian processes. Based on that, empirical studies in synthetic and real-world benchmarks further verify the effectiveness of the proposed parametric optimizer.","doi":"10.24963/ijcai.2026/708","url":"https://ijcai.org/proceedings/2026/708","pdf":"https://ijcai.org/proceedings/2026/0708.pdf","markdown":"https://ijcai.org/proceedings/2026/708.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/708","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Search","track":"Main Track","number":708,"pages":{"first":6361,"last":6369},"keywords":["Search: Evolutionary computation","Uncertainty in AI: Sequential decision making","Search: Search and machine learning"],"videos":[],"references":[{"position":1,"raw":"[Balandat et al., 2020] Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wilson, and Eytan Bakshy. 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