mlsanityData to 7 Oct 2026

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art

arXiv 2412.01566 ↗First posted 2 Dec 2024cs.LG math.OCcited by 6

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Abstract (arXiv)

Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the consideration of secondary goals such as sparsity, or multicriteria hyperparameter tuning. However - as multi-objective optimization is significantly more costly than single-objective optimization - the recent focus on deep learning architectures poses considerable additional challenges due to the very large number of parameters, strong nonlinearities and stochasticity. This survey covers recent advancements in the area of multi-objective deep learning. We introduce a taxonomy of existing methods - based on the type of training algorithm as well as the decision maker's needs - before listing recent advancements, and also successful applications. All three main learning paradigms supervised learning, unsupervised learning and reinforcement learning are covered, and we also address the recently very popular area of generative modeling.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 92 of its 198 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

92 of 198 references.

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50 most cited of 92 All 92 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyondcited by 15
  2. 2025SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusioncited by 2
  3. 2024Surrogate-assisted multi-objective design of complex multibody systemscited by 0
  4. 2025Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraintscited by 0
  5. 2026Interactive Pareto navigation for deep multi-task learningcited by 0
  6. 2026MOON: Multi-Objective OrthoNormalized Updates for Multitask Learningcited by 0