# A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

arXiv: https://arxiv.org/abs/2302.12487 · HTML: https://mlsanity.com/paper/2302.12487/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2023.

## Built on by (most cited first, 13 of 13)

- [A Brief Review of Hypernetworks in Deep Learning](https://mlsanity.com/paper/2306.06955/) (2023)
- [Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation](https://mlsanity.com/paper/2305.15984/) (2023)
- [A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints](https://mlsanity.com/paper/2402.05955/) (2024)
- [Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems](https://mlsanity.com/paper/2407.16828/) (2024)
- [Collaborative Pareto Set Learning in Multiple Multi-Objective Optimization Problems](https://mlsanity.com/paper/2404.01224/) (2024)
- [Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes](https://mlsanity.com/paper/2502.08282/) (2025)
- [A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks](https://mlsanity.com/paper/2506.06715/) (2025)
- [Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process](https://mlsanity.com/paper/2304.13671/) (2023)
- [A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming](https://mlsanity.com/paper/2401.08488/) (2024)
- [Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimization](https://mlsanity.com/paper/2408.05778/) (2024)
- [Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems](https://mlsanity.com/paper/2507.09566/) (2025)
- [Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentation](https://mlsanity.com/paper/2601.19365/) (2026)
- [Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization](https://mlsanity.com/paper/2605.12106/) (2026)

## Abstract (arXiv)

Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.