[2412.06154] Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions

Computer Science > Machine Learning arXiv:2412.06154 (cs) [Submitted on 9 Dec 2024 (v1), last revised 1 Apr 2026 (this version, v2)] Title:Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions Authors:Edward Chen, Natalie Dullerud, Thomas Niedermayr, Elizabeth Kidd, Ransalu Senanayake, Pang Wei Koh, Sanmi Koyejo, Carlos Guestrin View a PDF of the paper titled Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions, by Edward Chen and 7 other authors View PDF HTML (experimental) Abstract:Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal (PO) solution which aligns with their preferences. Evaluating individual solutions is often expensive, and the high-dimensional trade-off space makes exhaustive exploration of the full Pareto frontier (PF) infeasible. We introduce a novel, principled two-step process for obtaining a compact set of PO points that aligns with user preferences, which are specified a priori as general monotonic utility functions (MFs). Our process (1) densely samples the user's region of interest on the PF, then (2) sparsifies the results into a small, diverse set for the DM. We instantiate this framework with soft-hard functions (SHFs), an intuitive class of MFs that operationalizes the common expert heuristic of imposing soft and hard bounds. We provide extensive empirical validation of our framework instantiated with SHFs on diverse doma...