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[2602.15595] Multi-Objective Coverage via Constraint Active Search
Summary
This paper introduces a novel algorithm, MOC-CAS, for solving the multi-objective coverage problem, enhancing efficiency in applications like drug discovery and materials design.
Why It Matters
The multi-objective coverage problem is crucial for accelerating scientific discovery in fields such as drug development and materials science. The proposed MOC-CAS algorithm offers a more efficient approach to sample selection, potentially leading to faster and more effective research outcomes.
Key Takeaways
- MOC-CAS algorithm improves sample selection for multi-objective coverage.
- The approach is particularly beneficial in drug discovery and materials design.
- Empirical results show MOC-CAS outperforms existing methods on large-scale datasets.
Computer Science > Machine Learning arXiv:2602.15595 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 17 Feb 2026] Title:Multi-Objective Coverage via Constraint Active Search Authors:Zakaria Shams Siam, Xuefeng Liu, Chong Liu View a PDF of the paper titled Multi-Objective Coverage via Constraint Active Search, by Zakaria Shams Siam and 2 other authors View PDF HTML (experimental) Abstract:In this paper, we formulate the new multi-objective coverage (MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cover the feasible multi-objective space. This problem is of great importance in many critical real-world applications, e.g., drug discovery and materials design, as this representative set can be evaluated much faster than the whole feasible set, thus significantly accelerating the scientific discovery process. Existing works cannot be directly applied as they either focus on sample space coverage or multi-objective optimization that targets the Pareto front. However, chemically diverse samples often yield identical objective profiles, and safety constraints are usually defined on the objectives. To solve this MOC problem, we propose a novel sear...
