publications

2026

1. 

   Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization

   Jatan Shrestha^*, Santeri Heiskanen^*, Kari Hepola, and 3 more authors

   In The Fourteenth International Conference on Learning Representations, 2026

   Oral Abs arXiv Bib HTML Code Website

   This work was accepted as an oral presentation

   Multi-objective optimization (MOO) arises in many real-world applications where trade-offs between competing objectives must be carefully balanced. In the offline setting, where only a static dataset is available, the main challenge is generalizing beyond observed data. We introduce Pareto-Conditioned Diffusion (PCD), a novel framework that formulates offline MOO as a conditional sampling problem. By conditioning directly on desired trade-offs, PCD avoids the need for explicit surrogate models. To effectively explore the Pareto front, PCD employs a reweighting strategy that focuses on high-performing samples and a reference-direction mechanism to guide sampling towards novel, promising regions beyond the training data. Experiments on standard offline MOO benchmarks show that PCD achieves highly competitive performance and, importantly, demonstrates greater consistency across diverse tasks than existing offline MOO approaches

   @inproceedings{shrestha2026paretoconditioned,
     title = {Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization},
     author = {Shrestha, Jatan and Heiskanen, Santeri and Hepola, Kari and Rissanen, Severi and Jääskeläinen, Pekka and Pajarinen, Joni},
     booktitle = {The Fourteenth International Conference on Learning Representations},
     year = {2026},
   }