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Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond

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Pith's one-line read One taxonomy sorts gradient-based multi-objective deep learning into three output types.

desk verdict Useful, mostly accurate survey whose output-based taxonomy is a real contribution; the 'first survey' claim is overstated and the finite-set category needs cleanup, but it deserves peer review. read the letter →

arxiv 2501.10945 v3 pith:AJN75YBA submitted 2025-01-19 cs.LG stat.ML

Weiyu Chen , Baijiong Lin , Xiaoyuan Zhang , Xi Lin , Han Zhao , Qingfu Zhang , James T. Kwok This is my paper · ORCID
classification cs.LGstat.ML
keywords Multi-ObjectiveOptimizationMulti-TaskLearningParetoSetDeepGradientBalancingScalarizationHypernetworkLLMAlignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to prove that the scattered set of gradient-based methods for multi-objective deep learning is actually one field with a simple organizing question: what does the method give you back? It groups the literature into methods that return a single balanced Pareto-optimal model, methods that return a finite set of trade-off models, and methods that learn a continuous mapping from user preferences to models. Under this taxonomy it reviews convergence and generalization theory, applications from multi-task vision to large-language-model alignment, benchmark datasets and software libraries, and a list of open problems. A sympathetic reader would take away that the field has matured enough to be mapped, and that the map itself is the paper's central contribution.

What carries the argument

The central organizing device is the output-type taxonomy: single balanced solution versus finite set of Pareto-optimal solutions versus infinite continuous Pareto set learned as a preference-to-parameter map. The taxonomy carries the entire survey — every algorithm is placed into one of the three categories, the discussion of trade-offs (memory, cost, controllability) is organized around the categories, and the open problems are stated as gaps within or between them.

What would settle it

Find a published gradient-based MOO algorithm that produces a single solution during training but at evaluation time can return an arbitrary preference-specified Pareto-optimal model without retraining or an explicit learned preference map; if no category in the three-way taxonomy accommodates it, the taxonomy is not exhaustive. For the first-survey claim, a counter-example would be any earlier survey that already covers gradient-based MOO algorithms together with their theory and applications across all three output regimes.

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Extended reading notes

Core claim

The paper's central claim is that gradient-based multi-objective deep learning is best understood by the type of output an algorithm produces, not by its internal optimization mechanism. Single-solution methods, including loss balancing and gradient balancing, aim for one well-balanced Pareto-optimal model. Finite-set methods, whether they decompose the problem by preference vectors or directly optimize for diversity, produce a discrete approximation of the Pareto front. Infinite-set methods learn a preference-conditioned structure, such as a hypernetwork, a FiLM-conditioned network, or a parameter combination of base models, so that a user can generate a model for any preference on demand. The paper asserts this is the first survey devoted specifically to gradient-based multi-objective optimization (MOO) in deep learning, and it uses the three-way split to unify coverage of algorithms, theory, applications, resources, and open challenges.

Load-bearing premise

The survey's usefulness rests on the assumption that categorizing methods by the type of output they produce is the right and exhaustive way to organize the field, so that every real algorithm fits cleanly into one of the three boxes.

Editorial extensions

If this is right

  • A practitioner facing a new multi-objective problem can first ask what output is needed — one balanced model, a menu of trade-offs, or on-demand preference control — and then select from the corresponding family of methods.
  • Gradient-balancing methods that converge to Pareto stationarity are available at stochastic rates comparable to single-objective optimization, so the practical bottleneck is computational cost, not lack of convergence guarantees.
  • Generalization theory for multi-objective deep learning is younger than convergence theory; near-optimal sample complexity for Tchebycheff scalarization exists, and the paper calls for extending stability-based and architecture-aware generalization analyses.
  • The paper's open-problem list implies that the field's next steps are reducing gradient-balancing cost, handling many objectives, distributed training, and carrying multi-objective methods beyond RLHF into other stages of the LLM lifecycle.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The output-type taxonomy might apply equally well to gradient-free MOO, suggesting a unified comparison criterion across evolutionary and gradient-based methods: what kind of output the search returns, not how the search moves.
  • If infinite-set methods mature, the unit of deployment for a model could shift from a single checkpoint to a preference-conditioned artifact that is reconfigured at inference time, a change with serving and personalization consequences the paper only begins to explore through LLM alignment.
  • A directly testable prediction follows from the theory section: on the same benchmark (for example NYUv2 or QM9), single-solution gradient-balancing methods with Pareto-stationarity guarantees should show lower run-to-run variance in objective trade-offs than loss-balancing methods; the paper does not run this comparison.
  • The paper treats user preferences exclusively as a simplex vector; richer preference models such as constraints, rankings, or natural-language descriptions would likely require a new axis in the taxonomy, a direction the authors flag as open.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This paper is a survey of gradient-based multi-objective optimization (MOO) methods for deep learning. It proposes a taxonomy based on the type of output produced by an algorithm: (i) a single Pareto-optimal solution, (ii) a finite set of Pareto-optimal solutions, and (iii) an infinite/continuous Pareto set. For each category, it reviews representative algorithms, summarizes convergence and generalization theory, describes applications (reinforcement learning, Bayesian optimization, computer vision, neural architecture search, recommender systems, LLMs), and lists benchmark datasets and software libraries. The paper also claims to be the first survey focused specifically on gradient-based MOO methods in deep learning.

Significance. If the proposed taxonomy holds, this survey provides a useful organizing framework and a practical entry point for researchers, especially through its maintained GitHub resource list, its structured comparison of algorithms, and an accurate-in-main-lines account of convergence results. The output-based organizing principle is more discriminating than a chronological or method-by-method listing. The main weakness is that the taxonomy is not applied consistently: several algorithms in the ``finite set'' category do not produce a menu of trade-off solutions for user selection, which undermines the central claim of systematic categorization. The novelty claim also needs qualification in light of the acknowledged competing survey [141].

major comments (2)
  1. [Section 1.2 vs. Section 4.2 (Eqs. 31-32)] The taxonomy in Section 1.2 defines category (ii) as algorithms that ``obtain a finite set of Pareto-optimal solutions that allows users to select from multiple options based on their specific needs.'' However, Section 4.2 explicitly includes F4M (Eq. 31), SoM (Eq. 32), and MosT, which the text itself describes as addressing the ``converse case, where objectives outnumber solutions (m > n)'' and minimizing an aggregation of per-objective minima over an n-element set. These are coverage methods, not a discrete approximation of the Pareto front for user selection; their output is a coverage set. This contradicts the paper's central organizing principle and would mislead a reader who uses Figure 2 to locate methods. The authors should either move these methods to a separate category (e.g., ``coverage sets'') or explicitly revise the category definition and Figure 2 to accommodate this distinct subarea.
  2. [Section 1.1] The claim that this is ``the first survey paper focusing on the gradient-based MOO methods in deep learning'' is difficult to sustain because the paper itself acknowledges Peitz and Hotegni [141] as a survey of MOO algorithms for deep learning, albeit one that covers only a limited selection of gradient-based methods and omits theory and applications. The novelty claim should be qualified, for example as ``the first comprehensive survey covering algorithms, theory, and applications,'' and the comparison with [141] should be made more explicit so that the distinguishing features are clear.
minor comments (6)
  1. [Eq. (17)] The closed-form solution for IMTL-G as printed has a dimension mismatch: the expression λ_(2,...,m) = g_1^T U (D U^T)^{-1} yields a 1×d row vector, whereas λ_(2,...,m) should be an (m-1)-dimensional column vector. The correct form is likely (U^T D)^{-1} U^T g_1 (up to transposition); please correct the formula.
  2. [Section 3.2.3, Eq. (24)] The approximation G^T d ≈ (1/η)[f_1^(k)-f_1^(k+1), ..., f_m^(k)-f_m^(k+1)]^T is terse; adding one sentence that invokes the first-order Taylor expansion f_i(θ-ηd) ≈ f_i(θ) - η g_i^T d would make the derivation much easier to follow.
  3. [Page 1, ACM Reference Format] The ACM Reference Format line contains ``Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009'' and ``© 2018,'' which are evidently template artifacts; these should be updated or removed before publication.
  4. [Section 6.2] The sentence ``Haghtalab et al. are the first to show the sample complexity lower bound of Ω̃((v+m)/ε^2)'' is missing a citation for the specific work; please add the reference so the reader can locate the result.
  5. [Section 5.2, Eq. (37)] The notation g̃_α(ℓ(...)) is unclear because g̃_α is described as an MOO algorithm that produces a single solution, not a scalar loss function; please define how an algorithm is used inside the expectation, or rewrite the objective in a more standard operator form.
  6. [Figure 2] The box ``Methods without Using Preference Vectors'' lists GradHV, MOO-SVGD, F4M, SoM, and MosT together; until the taxonomy issue raised in Major Comment 1 is resolved, consider adding a footnote or visual distinction to indicate that the last three are coverage-based methods with a different goal.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy and cited theory results do not reduce to their own inputs.

full rationale

This paper is a survey, not a derivation: its central claim is a literature taxonomy (Section 1.2 and Figure 2), and its theoretical material (Section 6) reports published convergence and generalization results. I checked for each circular pattern. The taxonomy is defined by output type (single, finite, infinite), and methods are placed into those categories; even if Section 4.2's inclusion of the many-objective cover methods F4M/SoM/MosT is debatable because they optimize an aggregation of per-objective minima rather than a user-selectable menu of trade-offs, that is an internal-consistency or correctness concern, not a case where a conclusion is identical to its premise. The 'first survey' assertion in Section 1.1 is a priority claim supported by comparing with [141]; it is not a derived result and does not smuggle the conclusion into its inputs. The paper frequently cites work by its own authors (e.g., [18, 19, 20, 94, 102, 103, 229]), but these are surveyed entries rather than load-bearing justifications: no theoretical conclusion in Section 6 rests on an unpublished self-citation, and the convergence bounds cited (Fliege et al., Liu and Vicente, Zhou et al., Fernando et al., Chen et al., Xiao et al., Zhang et al.) are external published analyses. No parameter is fitted and then renamed a prediction, and no uniqueness theorem from the authors is invoked to force a choice. Accordingly, the survey's content is self-contained as an organizational and literature-summarizing claim, and no significant circularity is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

As a survey, the paper introduces no free parameters or invented entities. It rests on three types of assumptions: (1) that the surveyed papers are represented accurately, (2) that the three-output taxonomy is a meaningful and complete organizational schema, and (3) that the literature selection is representative. These are domain assumptions and one ad hoc organizing principle.

assumptions (3)
  • domain assumption The summarized descriptions of existing algorithms and theorems accurately reflect the original cited papers.
    The survey does not run experiments or reproduce derivations; it restates results from many cited sources, so correctness depends on faithful summarization.
  • ad hoc to paper The taxonomy of algorithm outputs (single solution, finite set, continuous/infinite set) is a meaningful and complete way to organize the field.
    This is the authors' own organizational contribution, introduced in Section 1.2 and Figure 2. If important algorithms do not fit these categories, the survey's structure is misleading.
  • domain assumption The literature selection (about 70+ papers and 13 datasets) is comprehensive and representative of the field.
    The survey claims to be comprehensive and first of its kind. This depends on the authors' search and selection strategy, which is not described in a systematic-review protocol.

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Cite this review

Pith. "Pith review of Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond." pith.science (2026). https://pith.science/paper/AJN75YBA

@misc{pith2026250110945,
  author       = {Pith},
  title        = {Pith review of: Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AJN75YBA}},
  note         = {Machine review of arXiv:2501.10945}
}
read the original abstract

Many modern deep learning applications require balancing multiple objectives that are often conflicting. Examples include multi-task learning, fairness-aware learning, and the alignment of Large Language Models (LLMs). This leads to multi-objective deep learning, which tries to find optimal trade-offs or Pareto-optimal solutions by adapting mathematical principles from the field of Multi-Objective Optimization (MOO). However, directly applying gradient-based MOO techniques to deep neural networks presents unique challenges, including high computational costs, optimization instability, and the difficulty of effectively incorporating user preferences. This paper provides a comprehensive survey of gradient-based techniques for multi-objective deep learning. We systematically categorize existing algorithms based on their outputs: (i) methods that find a single, well-balanced solution, (ii) methods that generate a finite set of diverse Pareto-optimal solutions, and (iii) methods that learn a continuous Pareto set of solutions. In addition to this taxonomy, the survey covers theoretical analyses, key applications, practical resources, and highlights open challenges and promising directions for future research. A comprehensive list of multi-objective deep learning algorithms is available at https://github.com/Baijiong-Lin/Awesome-Multi-Objective-Deep-Learning.

Figures

Figures reproduced from arXiv: 2501.10945 by the authors.

Figure 1
Figure 1. An overview of gradient-based multi-objective deep learning. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Taxonomy of existing gradient-based multi-objective deep learning algorithms. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Illustration of Pareto optimality concepts. The blue, yellow, and red circles denote Pareto optimal solutions, weakly Pareto [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Illustration of the update direction 𝒅 of several representative methods: EW, DWA [115] from loss balancing methods, MGDA [157] from gradient weighting methods, and PCGrad [217] from gradient manipulation methods. They are illustrated in a two￾objective learning proble…
Figure 5
Figure 5. Figure 5: Distributions of solutions obtained by using representative methods: PMTL [ [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Illustration of different structures to learn an infinite number of solutions. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

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Forward citations

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Reference graph

Works this paper leans on

243 extracted references · 62 canonical work pages · cited by 7 Pith papers

  1. [141]

    Sebastian Peitz and Sedjro Salomon Hotegni. 2024. Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. arXiv preprint arXiv:2412.01566 (2024)

    work page Pith review arXiv 2024

  2. [1]

    Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, and Eytan Bakshy. 2023. Unexpected improvements to expected improvement for bayesian optimization. In Conference on Neural Information Processing Systems

    2023

  3. [2]

    Baris Askin, Pranay Sharma, Gauri Joshi, and Carlee Joe-Wong. 2024. Federated Communication-Efficient Multi-Objective Optimization. arXiv preprint arXiv:2410.16398 (2024)

    work page Pith review arXiv 2024

  4. [3]

    Pranjal Awasthi, Nika Haghtalab, and Eric Zhao. 2023. Open problem: The sample complexity of multi-distribution learning for VC classes. In Annual Conference on Learning Theory

    2023

  5. [4]

    Qinbo Bai, Mridul Agarwal, and Vaneet Aggarwal. 2022. Joint optimization of concave scalarized multi-objective reinforcement learning with policy gradient based algorithm. Journal of Artificial Intelligence Research 74 (2022), 1565–1597

    2022

  6. [5]

    Hao Ban and Kaiyi Ji. 2024. Fair Resource Allocation in Multi-Task Learning. In International Conference on Machine Learning

    2024

  7. [6]

    Toygun Basaklar, Suat Gumussoy, and Umit Y Ogras. 2023. PD-MORL: Preference-driven multi-objective reinforcement learning algorithm. In International Conference on Learning Representations

    2023

  8. [7]

    Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa. 2019. Max-value entropy search for multi-objective Bayesian optimization. In Conference on Neural Information Processing Systems

    2019

Show all 243 references
  1. [8]

    Syrine Belakaria, Aryan Deshwal, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa. 2020. Uncertainty-aware search framework for multi-objective Bayesian optimization. In Annual AAAI Conference on Artificial Intelligence

    2020

  2. [9]

    Nicola Beume, Carlos M Fonseca, Manuel Lopez-Ibanez, Luis Paquete, and Jan Vahrenhold. 2009. On the complexity of computing the hypervolume indicator. IEEE Transactions on Evolutionary Computation 13, 5 (2009), 1075–1082

    2009

  3. [10]

    V Joseph Bowman Jr. 1976. On the relationship of the Tchebycheff norm and the efficient frontier of multiple-criteria objectives. InMultiple Criteria Decision Making: Proceedings of a Conference Jouy-en-Josas

    1976

  4. [11]

    Han Cai, Ligeng Zhu, and Song Han. 2019. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. In International Conference on Learning Representations

    2019

  5. [12]

    Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, and Jiayi Huang. 2024. A survey on mixture of experts.arXiv preprint arXiv:2407.06204 (2024)

    2024 arXiv

  6. [13]

    Jinbiao Chen, Jiahai Wang, Zizhen Zhang, Zhiguang Cao, Te Ye, and Siyuan Chen. 2023. Efficient meta neural heuristic for multi-objective combinatorial optimization. In Conference on Neural Information Processing Systems

    2023

  7. [14]

    Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu, Yining Ma, Te Ye, and Jiahai Wang. 2023. Neural multi-objective combinatorial optimization with diversity enhancement. In Conference on Neural Information Processing Systems . Manuscript submitted to ACM 28 Chen et al

    2023

  8. [15]

    Lisha Chen, Heshan Fernando, Yiming Ying, and Tianyi Chen. 2023. Three-way trade-off in multi-objective learning: Optimization, generalization and conflict-avoidance. In Conference on Neural Information Processing Systems

    2023

  9. [16]

    Lisha Chen, AFM Saif, Yanning Shen, and Tianyi Chen. 2024. FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning. In Conference on Neural Information Processing Systems

    2024

  10. [17]

    Shijie Chen, Yu Zhang, and Qiang Yang. 2024. Multi-task learning in natural language processing: An overview. Comput. Surveys 56, 12 (2024), 1–32

    2024

  11. [18]

    Weiyu Chen and James Kwok. 2022. Multi-Objective Deep Learning with Adaptive Reference Vectors. InConference on Neural Information Processing Systems

    2022

  12. [19]

    Weiyu Chen and James Kwok. 2024. Efficient Pareto Manifold Learning with Low-Rank Structure. InInternational Conference on Machine Learning

    2024

  13. [20]

    Weiyu Chen and James Kwok. 2024. You Only Merge Once: Learning the Pareto Set of Preference-Aware Model Merging. arXiv preprint arXiv:2408.12105 (2024)

    2024 arXiv

  14. [21]

    Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. 2018. GradNorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International Conference on Machine Learning

    2018

  15. [22]

    Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov. 2020. Just pick a sign: Optimizing deep multitask models with gradient sign dropout. In Conference on Neural Information Processing Systems

    2020

  16. [23]

    Ran Cheng, Yaochu Jin, Markus Olhofer, et al. 2016. Test problems for large-scale multiobjective and many-objective optimization.IEEE Transactions on Cybernetics 47, 12 (2016), 4108–4121

    2016

  17. [24]

    Eng Ung Choo and Derek R Atkins. 1983. Proper efficiency in nonconvex multicriteria programming. Mathematics of Operations Research 8, 3 (1983), 467–470

    1983

  18. [25]

    Carlos A Coello Coello, Gregorio Toscano Pulido, and Maximino Salazar Lechuga. 2004. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation 8, 3 (2004), 256–279

    2004

  19. [26]

    Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. 2016. The Cityscapes Dataset for Semantic Urban Scene Understanding. InIEEE/CVF Conference on Computer Vision and Pattern Recognition

    2016

  20. [27]

    Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, and Dmitry Storcheus. 2020. Agnostic learning with multiple objectives. In Conference on Neural Information Processing Systems

    2020

  21. [28]

    Michael Crawshaw. 2020. Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv:2009.09796 (2020)

    2020 arXiv

  22. [29]

    Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023. Ultrafeedback: Boosting language models with high-quality feedback. arXiv preprint arXiv:2310.01377 (2023)

    2023 arXiv

  23. [30]

    Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. 2020. Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization. In Conference on Neural Information Processing Systems

    2020

  24. [31]

    Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. 2021. Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement. In Conference on Neural Information Processing Systems

    2021

  25. [32]

    Sam Daulton, Maximilian Balandat, and Eytan Bakshy. 2023. Hypervolume knowledge gradient: a lookahead approach for multi-objective bayesian optimization with partial information. In International Conference on Machine Learning

    2023

  26. [33]

    Samuel Daulton, David Eriksson, Maximilian Balandat, and Eytan Bakshy. 2022. Multi-objective bayesian optimization over high-dimensional search spaces. In Conference on Uncertainty in Artificial Intelligence

    2022

  27. [34]

    Kalyanmoy Deb. 2011. Multi-objective optimisation using evolutionary algorithms: an introduction. In Multi-objective Evolutionary Optimisation for Product Design and Manufacturing . Springer, 3–34

    2011

  28. [35]

    Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197

    2002

  29. [36]

    Timo M Deist, Monika Grewal, Frank JWM Dankers, Tanja Alderliesten, and Peter AN Bosman. 2021. Multi-objective learning to predict Pareto fronts using hypervolume maximization. arXiv preprint arXiv:2102.04523 (2021)

    2021 arXiv

  30. [37]

    Timo M Deist, Stefanus C Maree, Tanja Alderliesten, and Peter AN Bosman. 2020. Multi-objective optimization by uncrowded hypervolume gradient ascent. In International Conference on Parallel Problem Solving From Nature

    2020

  31. [38]

    Jean-Antoine Désidéri. 2012. Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. Comptes Rendus Mathematique 350, 5-6 (2012), 313–318

    2012

  32. [39]

    Tommaso Di Noia, Jessica Rosati, Paolo Tomeo, and Eugenio Di Sciascio. 2017. Adaptive multi-attribute diversity for recommender systems. Information Sciences 382 (2017), 234–253

    2017

  33. [40]

    Nikolaos Dimitriadis, Pascal Frossard, and François Fleuret. 2023. Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models. In International Conference on Machine Learning

    2023

  34. [41]

    Nikolaos Dimitriadis, Pascal Frossard, and Francois Fleuret. 2025. Pareto low-rank adapters: Efficient multi-task learning with preferences. In International Conference on Learning Representations

    2025

  35. [42]

    Lisang Ding, Ziang Chen, Xinshang Wang, and Wotao Yin. 2024. Efficient Algorithms for Sum-of-Minimum Optimization. In International Conference on Machine Learning

    2024

  36. [43]

    Alexey Dosovitskiy and Josip Djolonga. 2020. You only train once: Loss-conditional training of deep networks. In International Conference on Learning Representations. Manuscript submitted to ACM Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, ...

    2020

  37. [44]

    Matthias Ehrgott. 2005. Multicriteria optimization. Springer

    2005

  38. [45]

    Gabriele Eichfelder. 2021. Twenty years of continuous multiobjective optimization in the twenty-first century. EURO Journal on Computational Optimization 9 (2021), 100014

    2021

  39. [46]

    Michael Emmerich, André Deutz, and Nicola Beume. 2007. Gradient-based/evolutionary relay hybrid for computing Pareto front approximations maximizing the S-metric. In Hybrid Metaheuristics: 4th International Workshop

    2007

  40. [47]

    Michael TM Emmerich, Kyriakos C Giannakoglou, and Boris Naujoks. 2006. Single-and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation 10, 4 (2006), 421–439

    2006

  41. [48]

    Florian Felten, El-Ghazali Talbi, and Grégoire Danoy. 2024. Multi-objective reinforcement learning based on decomposition: A taxonomy and framework. Journal of Artificial Intelligence Research 79 (2024), 679–723

    2024

  42. [49]

    Heshan Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, and Tianyi Chen. 2023. Mitigating gradient bias in multi-objective learning: A provably convergent approach. In International Conference on Learning Representations

    2023

  43. [50]

    Jörg Fliege and Benar Fux Svaiter. 2000. Steepest descent methods for multicriteria optimization. Mathematical Methods of Operations Research 51, 3 (2000), 479–494

    2000

  44. [51]

    Jörg Fliege, A Ismael F Vaz, and Luís Nunes Vicente. 2019. Complexity of gradient descent for multiobjective optimization. Optimization Methods and Software 34, 5 (2019), 949–959

    2019

  45. [52]

    Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, and Yongfeng Zhang. 2022. Toward Pareto efficient fairness- utility trade-off in recommendation through reinforcement learning. In ACM International Conference on Web Search and Data Mining

    2022

  46. [53]

    Saeed Ghadimi and Guanghui Lan. 2013. Stochastic first-and zeroth-order methods for nonconvex stochastic programming. SIAM Journal on Optimization 23, 4 (2013), 2341–2368

    2013

  47. [54]

    Hyojun Go, Yunsung Lee, Seunghyun Lee, Shinhyeok Oh, Hyeongdon Moon, and Seungtaek Choi. 2023. Addressing negative transfer in diffusion models. In Conference on Neural Information Processing Systems

    2023

  48. [55]

    Zi Gong, Hang Yu, Cong Liao, Bingchang Liu, Chaoyu Chen, and Jianguo Li. 2024. CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models. In Conference on Empirical Methods in Natural Language Processing

    2024

  49. [56]

    Andreia P Guerreiro, Carlos M Fonseca, Michael TM Emmerich, et al. 2012. A Fast Dimension-Sweep Algorithm for the Hypervolume Indicator in Four Dimensions. In CCCG

    2012

  50. [57]

    Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, et al. 2024. Controllable preference optimization: Toward controllable multi-objective alignment. In Conference on Empirical Methods in Natural Language Processing

    2024

  51. [58]

    Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada, and Matthew Lease. 2022. Learning a neural Pareto manifold extractor with constraints. In Conference on Uncertainty in Artificial Intelligence

    2022

  52. [59]

    David Ha, Andrew Dai, and Quoc V Le. 2016. Hypernetworks. arXiv preprint arXiv:1609.09106 (2016)

    2016 arXiv

  53. [60]

    Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. 2023. Efficient diffusion training via min-snr weighting strategy. In International Conference on Computer Vision

    2023

  54. [61]

    Daniel Hernández-Lobato, Jose Hernandez-Lobato, Amar Shah, and Ryan Adams. 2016. Predictive entropy search for multi-objective bayesian optimization. In International Conference on Machine Learning

    2016

  55. [62]

    Long P Hoang, Dung D Le, Tran Anh Tuan, and Tran Ngoc Thang. 2023. Improving Pareto front learning via multi-sample hypernetworks. In Annual AAAI Conference on Artificial Intelligence

    2023

  56. [63]

    Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020. XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation. In International Conference on Machine Learning

    2020

  57. [64]

    Yuzheng Hu, Ruicheng Xian, Qilong Wu, Qiuling Fan, Lang Yin, and Han Zhao. 2023. Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective. In Conference on Neural Information Processing Systems

    2023

  58. [65]

    Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, and Yaoliang Yu. 2022. Federated learning meets multi-objective optimization. IEEE Transactions on Network Science and Engineering 9, 4 (2022), 2039–2051

    2022

  59. [66]

    Carl Hvarfner, Frank Hutter, and Luigi Nardi. 2022. Joint entropy search for maximally-informed Bayesian optimization. In Conference on Neural Information Processing Systems

    2022

  60. [67]

    Youngsik Hwang and Dong-Young Lim. 2024. Dual Cone Gradient Descent for Training Physics-Informed Neural Networks. In Conference on Neural Information Processing Systems

    2024

  61. [68]

    Keishi Ishihara, Anssi Kanervisto, Jun Miura, and Ville Hautamaki. 2021. Multi-task learning with attention for end-to-end autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2021

  62. [69]

    Martin Jaggi. 2013. Revisiting Frank-Wolfe: Projection-free sparse convex optimization. In International Conference on Machine Learning

    2013

  63. [70]

    Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcıa, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio. 2023. Multi-objective GFlowNets. In International Conference on Machine Learning

    2023

  64. [71]

    Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, and Prithviraj Am- manabrolu. 2023. Personalized soups: Personalized large language model alignment via post-hoc parameter merging.arXiv preprint arXiv:2310....

    2023 arXiv

  65. [72]

    Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Qiu, Boxun Li, and Yaodong Yang. 2024. PKU-SafeRLHF: Towards multi-level safety alignment for LLMs with human preference. arXiv preprint arXiv:2406.15513 (2024). Manuscript submitted to ACM 30 ...

    2024 arXiv

  66. [73]

    Donald R Jones, Matthias Schonlau, and William J Welch. 1998. Efficient global optimization of expensive black-box functions. Journal of Global Optimization 13 (1998), 455–492

    1998

  67. [74]

    Yan Kang, Hanlin Gu, Xingxing Tang, Yuanqin He, Yuzhu Zhang, Jinnan He, Yuxing Han, Lixin Fan, Kai Chen, and Qiang Yang. 2024. Optimizing Privacy, Utility, and Efficiency in a Constrained Multi-Objective Federated Learning Framework. ACM Transactions on Intelligent Systems and...

    2024

  68. [75]

    Alex Kendall, Yarin Gal, and Roberto Cipolla. 2018. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2018

  69. [76]

    Joshua Knowles. 2006. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 10, 1 (2006), 50–66

    2006

  70. [77]

    Tamara G Kolda and Brett W Bader. 2009. Tensor decompositions and applications. SIAM review 51, 3 (2009), 455–500

    2009

  71. [78]

    Mina Konakovic Lukovic, Yunsheng Tian, and Wojciech Matusik. 2020. Diversity-guided multi-objective bayesian optimization with batch evaluations. In Conference on Neural Information Processing Systems

    2020

  72. [79]

    Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Technical Report (2009)

    2009

  73. [80]

    Panagiotis Kyriakis and Jyotirmoy Deshmukh. 2022. Pareto policy adaptation. In International Conference on Learning Representations

    2022

  74. [81]

    Sunggu Kyung, Jongjun Won, Seongyong Pak, Sunwoo Kim, Sangyoon Lee, Kanggil Park, Gil-Sun Hong, and Namkug Kim. 2024. Generative Adversarial Network with Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising. IEEE Transactions on Medical Imaging (2024)

    2024

  75. [82]

    Anisio Lacerda. 2017. Multi-objective ranked bandits for recommender systems. Neurocomputing 246 (2017), 12–24

    2017

  76. [83]

    Song Lai, Zhe Zhao, Fei Zhu, Xi Lin, Qingfu Zhang, and Gaofeng Meng. 2025. Pareto Continual Learning: Preference-Conditioned Learning and Adaption for Dynamic Stability-Plasticity Trade-off. In Annual AAAI Conference on Artificial Intelligence

    2025

  77. [84]

    Bingdong Li, Zixiang Di, Yongfan Lu, Hong Qian, Feng Wang, Peng Yang, Ke Tang, and Aimin Zhou. 2024. Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models. arXiv preprint arXiv:2405.08674 (2024)

    2024 arXiv

  78. [85]

    Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, and Ali Jadbabaie. 2023. Convex and non-convex optimization under generalized smoothness. In Conference on Neural Information Processing Systems

    2023

  79. [86]

    Kaiwen Li, Tao Zhang, and Rui Wang. 2020. Deep reinforcement learning for multiobjective optimization. IEEE Transactions on Cybernetics 51, 6 (2020), 3103–3114

    2020

  80. [87]

    Weiqi Li, Fan Lyu, Fanhua Shang, Liang Wan, and Wei Feng. 2024. Long-Tailed Learning as Multi-Objective Optimization. In Annual AAAI Conference on Artificial Intelligence

    2024

  81. [88]

    Wei-Hong Li, Xialei Liu, and Hakan Bilen. 2024. Universal representations: A unified look at multiple task and domain learning. International Journal of Computer Vision 132, 5 (2024), 1521–1545

    2024

  82. [89]

    Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, and Tianyi Zhou. 2025. Many-Objective Multi-Solution Transport. In International Conference on Learning Representations

    2025

  83. [90]

    Xiaodan Liang, Xiwen Liang, and Hang Xu. 2023. Multi-task Perception for Autonomous Driving. In Autonomous Driving Perception: Fundamentals and Applications

    2023

  84. [91]

    Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, and Ying-Cong Chen. 2025. MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)

    2025

  85. [92]

    Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, and Ying-Cong Chen. 2024. MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders. In European Conference on Computer Vision

    2024

  86. [93]

    Baijiong Lin, Weisen Jiang, Yuancheng Xu, Hao Chen, and Ying-Cong Chen. 2025. PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model. In International Conference on Machine Learning

    2025

  87. [94]

    Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu, and James T. Kwok. 2023. Dual-Balancing for Multi-Task Learning. arXiv preprint arXiv:2308.12029 (2023)

    2023

  88. [95]

    Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor Tsang. 2022. Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning. Transactions on Machine Learning Research (2022)

    2022

  89. [96]

    Baijiong Lin and Yu Zhang. 2023. LibMTL: A Python library for deep multi-task learning. Journal of Machine Learning Research 24, 209 (2023), 1–7

    2023

  90. [97]

    Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, and Peng Jiang. 2019. A Pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In ACM Conference on Recommender Systems

    2019

  91. [98]

    Xi Lin, Yilu Liu, Xiaoyuan Zhang, Fei Liu, Zhenkun Wang, and Qingfu Zhang. 2025. Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization. In International Conference on Learning Representations

    2025

  92. [99]

    Xi Lin, Zhiyuan Yang, and Qingfu Zhang. 2022. Pareto Set Learning for Neural Multi-objective Combinatorial Optimization. In International Conference on Learning Representations

    2022

  93. [100]

    Xi Lin, Zhiyuan Yang, Qingfu Zhang, and Sam Kwong. 2020. Controllable Pareto Multi-Task Learning. arXiv preprint arXiv:2010.06313 (2020)

    2020 arXiv

  94. [101]

    Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, and Qingfu Zhang. 2022. Pareto set learning for expensive multi-objective optimization. In Conference on Neural Information Processing Systems

    2022

  95. [102]

    Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, and Qingfu Zhang. 2024. Smooth Tchebycheff Scalarization for Multi-Objective Optimization. In International Conference on Machine Learning . Manuscript submitted to ACM Gradient-Based Multi-Objective Deep Learning: A...

    2024

  96. [103]

    Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. 2019. Pareto multi-task learning. In Conference on Neural Information Processing Systems

    2019

  97. [104]

    Bingchang Liu, Chaoyu Chen, Zi Gong, Cong Liao, Huan Wang, Zhichao Lei, Ming Liang, Dajun Chen, Min Shen, Hailian Zhou, Wei Jiang, Hang Yu, and Jianguo Li. 2024. MFTCoder: Boosting code LLMs with multitask fine-tuning. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining

    2024

  98. [105]

    Bo Liu, Yihao Feng, Peter Stone, and Qiang Liu. 2023. FAMO: Fast adaptive multitask optimization. In Conference on Neural Information Processing Systems

    2023

  99. [106]

    Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. 2021. Conflict-averse gradient descent for multi-task learning. In Conference on Neural Information Processing Systems

    2021

  100. [107]

    Erlong Liu, Yu-Chang Wu, Xiaobin Huang, Chengrui Gao, Ren-Jian Wang, Ke Xue, and Chao Qian. 2025. Pareto Set Learning for Multi-Objective Reinforcement Learning. In Annual AAAI Conference on Artificial Intelligence

    2025

  101. [108]

    Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. DARTS: Differentiable Architecture Search. In International Conference on Learning Representations

    2019

  102. [109]

    Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. 2021. Towards Impartial Multi-task Learning. In International Conference on Learning Representations

    2021

  103. [110]

    Meitong Liu, Xiaoyuan Zhang, Chulin Xie, Kate Donahue, and Han Zhao. 2024. Online Mirror Descent for Tchebycheff Scalarization in Multi- Objective Optimization. arXiv preprint arXiv:2410.21764 (2024)

    2024

  104. [111]

    Qiang Liu, Mengyu Chu, and Nils Thuerey. 2025. ConFIG: Towards conflict-free training of physics informed neural networks. In International Conference on Learning Representations

    2025

  105. [112]

    Qiang Liu and Dilin Wang. 2016. Stein variational gradient descent: A general purpose bayesian inference algorithm. In Conference on Neural Information Processing Systems

    2016

  106. [113]

    Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, and Jin Zhang. 2021. A value-function-based interior-point method for non-convex bi-level optimization. In International Conference on Machine Learning

    2021

  107. [114]

    Shikun Liu, Stephen James, Andrew Davison, and Edward Johns. 2022. Auto-Lambda: Disentangling Dynamic Task Relationships. Transactions on Machine Learning Research (2022)

    2022

  108. [115]

    Shikun Liu, Edward Johns, and Andrew J. Davison. 2019. End-To-End Multi-Task Learning With Attention. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2019

  109. [116]

    Suyun Liu and Luis Nunes Vicente. 2021. The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning. Annals of Operations Research (2021), 1–30

    2021

  110. [117]

    Xingchao Liu, Xin Tong, and Qiang Liu. 2021. Profiling Pareto front with multi-objective stein variational gradient descent. InConference on Neural Information Processing Systems

    2021

  111. [118]

    Yilu Liu, Chengyu Lu, Xi Lin, and Qingfu Zhang. 2024. Many-Objective Cover Problem: Discovering Few Solutions to Cover Many Objectives. In International Conference on Parallel Problem Solving From Nature

    2024

  112. [119]

    Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep learning face attributes in the wild. In International Conference on Computer Vision

    2015

  113. [120]

    Haoye Lu, Daniel Herman, and Yaoliang Yu. 2023. Multi-objective reinforcement learning: Convexity, stationarity and Pareto optimality. In International Conference on Learning Representations

    2023

  114. [121]

    Yongfan Lu, Bingdong Li, and Aimin Zhou. 2024. Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization. In Annual AAAI Conference on Artificial Intelligence

    2024

  115. [122]

    Pingchuan Ma, Tao Du, and Wojciech Matusik. 2020. Efficient continuous Pareto exploration in multi-task learning. In International Conference on Machine Learning

    2020

  116. [123]

    Debabrata Mahapatra and Vaibhav Rajan. 2020. Multi-task learning with user preferences: Gradient descent with controlled ascent in Pareto optimization. In International Conference on Machine Learning

    2020

  117. [124]

    Debabrata Mahapatra and Vaibhav Rajan. 2021. Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making. arXiv preprint arXiv:2108.00597 (2021)

    2021 arXiv

  118. [125]

    R Timothy Marler and Jasbir S Arora. 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26 (2004), 369–395

    2004

  119. [126]

    Natalia Martinez, Martin Bertran, and Guillermo Sapiro. 2020. Minimax pareto fairness: A multi objective perspective. In International Conference on Machine Learning. PMLR, 6755–6764

    2020

  120. [127]

    Kaisa Miettinen. 1999. Nonlinear multiobjective optimization. Vol. 12. Springer Science & Business Media

    1999

  121. [128]

    Nikola Milojkovic, Diego Antognini, Giancarlo Bergamin, Boi Faltings, and Claudiu Musat. 2019. Multi-gradient descent for multi-objective recommender systems. arXiv preprint arXiv:2001.00846 (2019)

    2019 arXiv

  122. [129]

    Blagoj Mitrevski, Milena Filipovic, Diego Antognini, Emma Lejal Glaude, Boi Faltings, and Claudiu Musat. 2020. Momentum-based gradient methods in multi-objective recommendation. arXiv preprint arXiv:2009.04695 (2020)

    2020 arXiv

  123. [130]

    Michinari Momma, Chaosheng Dong, and Jia Liu. 2022. A multi-objective/multi-task learning framework induced by Pareto stationarity. In International Conference on Machine Learning . Manuscript submitted to ACM 32 Chen et al

    2022

  124. [131]

    Hiroaki Mukai. 1980. Algorithms for multicriterion optimization. IEEE Trans. Automat. Control 25, 2 (1980), 177–186

    1980

  125. [132]

    John Nash. 1953. Two-person cooperative games. Econometrica: Journal of the Econometric Society (1953), 128–140

    1953

  126. [133]

    Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya. 2022. Multi-Task Learning as a Bargaining Game. In International Conference on Machine Learning

    2022

  127. [134]

    Aviv Navon, Aviv Shamsian, Ethan Fetaya, and Gal Chechik. 2021. Learning the Pareto Front with Hypernetworks. In International Conference on Learning Representations

    2021

  128. [135]

    Yurii Nesterov. 2013. Introductory lectures on convex optimization: A basic course . Vol. 87. Springer Science & Business Media

    2013

  129. [136]

    Minh-Duc Nguyen, Phuong Mai Dinh, Quang-Huy Nguyen, Long P Hoang, and Dung D Le. 2024. Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks. In Annual AAAI Conference on Artificial Intelligence

    2024

  130. [137]

    Biswajit Paria, Kirthevasan Kandasamy, and Barnabás Póczos. 2020. A flexible framework for multi-objective bayesian optimization using random scalarizations. In Conference on Uncertainty in Artificial Intelligence

    2020

  131. [138]

    Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, and Marcello Restelli. 2014. Policy gradient approaches for multi-objective sequential decision making. In International Joint Conference on Neural Networks

    2014

  132. [139]

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, et al . 2019. PyTorch: An Imperative Style, High-Performance Deep Learn...

    2019

  133. [140]

    Shubham Patil, Debopriyo Banerjee, and Shamik Sural. 2021. A graph theoretic approach for multi-objective budget constrained capsule wardrobe recommendation. ACM Transactions on Information Systems 40, 1 (2021), 1–33

    2021

  134. [142]

    Binghui Peng. 2024. The sample complexity of multi-distribution learning. In Annual Conference on Learning Theory

    2024

  135. [143]

    Nianli Peng and Brandon Fain. 2023. Nonlinear Multi-objective Reinforcement Learning with Provable Guarantees. arXiv preprint arXiv:2311.02544 (2023)

    2023 arXiv

  136. [144]

    Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018. FiLM: Visual reasoning with a general conditioning layer. In Annual AAAI Conference on Artificial Intelligence

    2018

  137. [145]

    Matteo Pirotta, Simone Parisi, and Marcello Restelli. 2015. Multi-objective reinforcement learning with continuous Pareto frontier approximation. In Annual AAAI Conference on Artificial Intelligence

    2015

  138. [146]

    Wolfgang Ponweiser, Tobias Wagner, Dirk Biermann, and Markus Vincze. 2008. Multiobjective optimization on a limited budget of evaluations using model-assisted-metric selection. In International Conference on Parallel Problem Solving From Nature

    2008

  139. [147]

    Pierre Quinton and Valérian Rey. 2024. Jacobian Descent for Multi-Objective Optimization. arXiv preprint arXiv:2406.16232 (2024)

    2024 arXiv

  140. [148]

    Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data 1, 1 (2014), 1–7

    2014

  141. [149]

    Alexandre Rame, Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier, and Matthieu Cord. 2023. Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards. In Conference on Neural Information Proce...

    2023

  142. [150]

    Dripta S Raychaudhuri, Yumin Suh, Samuel Schulter, Xiang Yu, Masoud Faraki, Amit K Roy-Chowdhury, and Manmohan Chandraker. 2022. Controllable dynamic multi-task architectures. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2022

  143. [151]

    Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. 2021. A comprehensive survey of neural architecture search: Challenges and solutions. Comput. Surveys 54, 4 (2021), 1–34

    2021

  144. [152]

    Diederik M Roijers. 2016. Multi-objective decision-theoretic planning. AI Matters 2, 4 (2016), 11–12

    2016

  145. [153]

    Diederik M Roijers, Shimon Whiteson, and Frans A Oliehoek. 2014. Linear support for multi-objective coordination graphs. In International Conference on Autonomous Agents and Multiagent Systems

    2014

  146. [154]

    Michael Ruchte and Josif Grabocka. 2021. Scalable Pareto front approximation for deep multi-objective learning. In IEEE International Conference on Data Mining

    2021

  147. [155]

    S Ruder. 2017. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv preprint arXiv:1706.05098 (2017)

    2017 arXiv

  148. [156]

    Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. 2010. Adapting visual category models to new domains. In European Conference on Computer Vision

    2010

  149. [157]

    Ozan Sener and Vladlen Koltun. 2018. Multi-task learning as multi-objective optimization. In Conference on Neural Information Processing Systems

    2018

  150. [158]

    Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov, and Anton Konushin. 2023. Independent component alignment for multi-task learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2023

  151. [159]

    Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas. 2015. Taking the human out of the loop: A review of Bayesian optimization. Proc. IEEE 104, 1 (2015), 148–175

    2015

  152. [160]

    Ke Shang, Hisao Ishibuchi, Linjun He, and Lie Meng Pang. 2020. A survey on the hypervolume indicator in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 25, 1 (2020), 1–20

    2020

  153. [161]

    Ruizhe Shi, Yifang Chen, Yushi Hu, ALisa Liu, Hannaneh Hajishirzi, Noah A Smith, and Simon S Du. 2024. Decoding-time language model alignment with multiple objectives. In Conference on Neural Information Processing Systems . Manuscript submitted to ACM Gradient-Based Multi-Obj...

    2024

  154. [162]

    Tianye Shu, Ke Shang, Cheng Gong, Yang Nan, and Hisao Ishibuchi. 2024. Learning Pareto Set for Multi-Objective Continuous Robot Control. In International Joint Conference on Artificial Intelligence

    2024

  155. [163]

    Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. 2012. Indoor segmentation and support inference from RGBD images. In European Conference on Computer Vision

    2012

  156. [164]

    Víctor Adrián Sosa Hernández, Oliver Schütze, Hao Wang, André Deutz, and Michael Emmerich. 2020. The Set-Based Hypervolume Newton Method for Bi-Objective Optimization. IEEE Transactions on Cybernetics 50, 5 (2020), 2186–2196

    2020

  157. [165]

    Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. 2020. Which tasks should be learned together in multi-task learning?. In International Conference on Machine Learning

    2020

  158. [166]

    Ralph E Steuer and Eng-Ung Choo. 1983. An interactive weighted Tchebycheff procedure for multiple objective programming. Mathematical Programming 26 (1983), 326–344

    1983

  159. [167]

    Ralph E Steuer and Paul Na. 2003. Multiple criteria decision making combined with finance: A categorized bibliographic study. European Journal of Operational Research 150, 3 (2003), 496–515

    2003

  160. [168]

    Peter Súkeník and Christoph Lampert. 2024. Generalization in multi-objective machine learning. Neural Computing and Applications (2024), 1–15

    2024

  161. [169]

    Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, and Frank Hutter. 2025. Multi-objective Differentiable Neural Architecture Search. In International Conference on Learning Representations

    2025

  162. [170]

    Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, and Masayuki Karasuyama. 2020. Multi-objective Bayesian optimization using Pareto-frontier entropy. In International Conference on Machine Learning

    2020

  163. [171]

    Anke Tang, Li Shen, Yong Luo, Shiwei Liu, Han Hu, and Bo Du. 2024. Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion. arXiv preprint arXiv:2406.09770 (2024)

    2024 arXiv

  164. [172]

    William Thomson. 1994. Cooperative models of bargaining. Handbook of Game Theory with Economic Applications 2 (1994), 1237–1284

    1994

  165. [173]

    Ben Tu, Axel Gandy, Nikolas Kantas, and Behrang Shafei. 2022. Joint entropy search for multi-objective bayesian optimization. In Conference on Neural Information Processing Systems

    2022

  166. [174]

    Tran Anh Tuan, Nguyen Viet Dung, and Tran Ngoc Thang. 2024. A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints. arXiv preprint arXiv:2402.05955 (2024)

    2024 arXiv

  167. [175]

    Gwo-Hshiung Tzeng, Cheng-Wei Lin, and Serafim Opricovic. 2005. Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy 33, 11 (2005), 1373–1383

    2005

  168. [176]

    Kristof Van Moffaert, Madalina M Drugan, and Ann Nowé. 2013. Hypervolume-based multi-objective reinforcement learning. In International Conference on Evolutionary Multi-Criterion Optimization

    2013

  169. [177]

    Kristof Van Moffaert, Madalina M Drugan, and Ann Nowé. 2013. Scalarized multi-objective reinforcement learning: Novel design techniques. In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

    2013

  170. [178]

    Kristof Van Moffaert and Ann Nowé. 2014. Multi-objective reinforcement learning using sets of Pareto dominating policies. Journal of Machine Learning Research 15, 1 (2014), 3483–3512

    2014

  171. [179]

    Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, and Luc Van Gool. 2021. Multi-task learning for dense prediction tasks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7 (2021), 3614–3633

    2021

  172. [180]

    Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. 2017. Deep hashing network for unsupervised domain adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2017

  173. [181]

    Dilin Wang, Meng Li, Chengyue Gong, and Vikas Chandra. 2021. AttentiveNAS: Improving neural architecture search via attentive sampling. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2021

  174. [182]

    Hongze Wang. 2024. Multi-objective reinforcement learning based on nonlinear scalarization and long-short-term optimization. Robotic Intelligence and Automation (2024)

    2024

  175. [183]

    Hao Wang, André Deutz, Thomas Bäck, and Michael Emmerich. 2017. Hypervolume indicator gradient ascent multi-objective optimization. In International Conference on Evolutionary Multi-Criterion Optimization

    2017

  176. [184]

    Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, and Tong Zhang. 2024. Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. In Annual Meeting of the Association for Computational...

    2024

  177. [185]

    Haoxiang Wang, Han Zhao, and Bo Li. 2021. Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation. In International Conference on Machine Learning

    2021

  178. [186]

    Xu Wang, Sen Wang, Xingxing Liang, Dawei Zhao, Jincai Huang, Xin Xu, Bin Dai, and Qiguang Miao. 2024. Deep Reinforcement Learning: A Survey. IEEE Transactions on Neural Networks and Learning Systems 35, 4 (2024), 5064–5078

    2024

  179. [187]

    Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023. Aligning large language models with human: A survey. arXiv preprint arXiv:2307.12966 (2023)

    2023 arXiv

  180. [188]

    Zhilin Wang, Yi Dong, Olivier Delalleau, Jiaqi Zeng, Gerald Shen, Daniel Egert, Jimmy J Zhang, Makesh Narsimhan Sreedhar, and Oleksii Kuchaiev

  181. [189]

    Zhenyu Wang, Yali Li, Hengshuang Zhao, and Shengjin Wang. 2024. One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection. In Conference on Neural Information Processing Systems . Manuscript submitted to ACM 34 Chen et al

    2024

  182. [190]

    Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao. 2021. Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models. In International Conference on Learning Representations

    2021

  183. [191]

    Zhenkun Wang, Shunyu Yao, Genghui Li, and Qingfu Zhang. 2023. Multiobjective combinatorial optimization using a single deep reinforcement learning model. IEEE Transactions on Cybernetics (2023)

    2023

  184. [192]

    Tingyang Wei, Shibin Wang, Jinghui Zhong, Dong Liu, and Jun Zhang. 2021. A review on evolutionary multitask optimization: Trends and challenges. IEEE Transactions on Evolutionary Computation 26, 5 (2021), 941–960

    2021

  185. [193]

    Timo Wilm, Philipp Normann, and Felix Stepprath. 2024. Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems. In ACM Conference on Recommender Systems

    2024

  186. [194]

    Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. 2019. FBNet: Hardware-aware efficient convnet design via differentiable neural architecture search. In IEEE/CVF Conference on Computer Vision ...

    2019

  187. [195]

    Yan Wu, Zhiwu Huang, Suryansh Kumar, Rhea Sanjay Sukthanker, Radu Timofte, and Luc Van Gool. 2021. Trilevel neural architecture search for efficient single image super-resolution. arXiv preprint arXiv:2101.06658 (2021)

    2021 arXiv

  188. [196]

    Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023. Fine-grained human feedback gives better rewards for language model training. In Conference on Neural Information Processing Systems

    2023

  189. [197]

    Peiyao Xiao, Hao Ban, and Kaiyi Ji. 2023. Direction-oriented multi-objective learning: Simple and provable stochastic algorithms. In Conference on Neural Information Processing Systems

    2023

  190. [198]

    Ruobing Xie, Yanlei Liu, Shaoliang Zhang, Rui Wang, Feng Xia, and Leyu Lin. 2021. Personalized approximate Pareto-efficient recommendation. In International World Wide Web Conference

    2021

  191. [199]

    Tao Xie, Kun Dai, Qihao Sun, Zhiqiang Jiang, Chuqing Cao, Lijun Zhao, Ke Wang, and Ruifeng Li. 2024. CO-Net++: A Cohesive Network for Multiple Point Cloud Tasks at Once with Two-Stage Feature Rectification. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    2024

  192. [200]

    Tao Xie, Shiguang Wang, Ke Wang, Linqi Yang, Zhiqiang Jiang, Xingcheng Zhang, Kun Dai, Ruifeng Li, and Jian Cheng. 2023. Poly-PC: A polyhedral network for multiple point cloud tasks at once. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2023

  193. [201]

    Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, and Wojciech Matusik. 2020. Prediction-guided multi-objective reinforcement learning for continuous robot control. In International Conference on Machine Learning

    2020

  194. [202]

    Mingjing Xu, Peizhong Ju, Jia Liu, and Haibo Yang. 2025. PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization. In Annual AAAI Conference on Artificial Intelligence

    2025

  195. [203]

    Xiaogang Xu, Hengshuang Zhao, Vibhav Vineet, Ser-Nam Lim, and Antonio Torralba. 2022. MTFormer: Multi-task learning via transformer and cross-task reasoning. In European Conference on Computer Vision

    2022

  196. [204]

    Zhiyuan Xu, Yinhe Chen, Huan-ang Gao, Weiyan Zhao, Guiyu Zhang, and Hao Zhao. 2025. Diffusion-based Visual Anagram as Multi-task Learning. In IEEE/CVF Winter Conference on Applications of Computer Vision

    2025

  197. [205]

    Kaifeng Yang, Michael Emmerich, André Deutz, and Thomas Bäck. 2019. Efficient computation of expected hypervolume improvement using box decomposition algorithms. Journal of Global Optimization 75 (2019), 3–34

    2019

  198. [206]

    Kaifeng Yang, Michael Emmerich, André Deutz, and Thomas Bäck. 2019. Multi-objective Bayesian global optimization using expected hypervolume improvement gradient. Swarm and Evolutionary Computation 44 (2019), 945–956

    2019

  199. [207]

    Liangwei Yang, Zhiwei Liu, Jianguo Zhang, Rithesh Murthy, Shelby Heinecke, Huan Wang, Caiming Xiong, and Philip S Yu. 2024. Personalized Multi-task Training for Recommender System. arXiv preprint arXiv:2407.21364 (2024)

    2024 arXiv

  200. [208]

    Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, and Jianshu Chen. 2024. Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment. In International Conference on Machine Learning

    2024

  201. [209]

    Yinghua Yao, Yuangang Pan, Jing Li, Ivor Tsang, and Xin Yao. 2024. PROUD: PaRetO-gUided diffusion model for multi-objective generation. Machine Learning 113, 9 (2024), 6511–6538

    2024

  202. [210]

    Dongqiangzi Ye, Yufei Xie, Weijia Chen, Zixiang Zhou, Lingting Ge, and Hassan Foroosh. 2024. LPFormer: LiDAR pose estimation transformer with multi-task network. In IEEE International Conference on Robotics & Automation

    2024

  203. [211]

    Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, and Ivor Tsang. 2024. A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization. In European Conference on Artificial Intelligence

    2024

  204. [212]

    Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, and Yu Zhang. 2021. Multi-objective meta learning. In Conference on Neural Information Processing Systems

    2021

  205. [213]

    Feiyang Ye, Baijiong Lin, Zhixiong Yue, Yu Zhang, and Ivor Tsang. 2024. Multi-Objective Meta-Learning. Artificial Intelligence 335 (2024), 104184

    2024

  206. [214]

    Hanrong Ye and Dan Xu. 2022. Inverted pyramid multi-task transformer for dense scene understanding. In European Conference on Computer Vision

    2022

  207. [215]

    Jun Yu, Yutong Dai, Xiaokang Liu, Jin Huang, Yishan Shen, Ke Zhang, Rong Zhou, Eashan Adhikarla, Wenxuan Ye, Yixin Liu, et al. 2024. Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras. arXiv prep...

    2024 arXiv

  208. [216]

    Runsheng Yu, Weiyu Chen, Xinrun Wang, and James Kwok. 2023. Enhancing meta learning via multi-objective soft improvement functions. In International Conference on Learning Representations . Manuscript submitted to ACM Gradient-Based Multi-Objective Deep Learning: Algorithms, T...

    2023

  209. [217]

    Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. 2020. Gradient surgery for multi-task learning. In Conference on Neural Information Processing Systems

    2020

  210. [218]

    Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. 2020. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning. In Conference on Robot Learning

    2020

  211. [219]

    Zhixiong Yue, Baijiong Lin, Yu Zhang, and Christy Liang. 2022. Effective, efficient and robust neural architecture search. In International Joint Conference on Neural Networks

    2022

  212. [220]

    Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese. 2018. Taskonomy: Disentangling task transfer learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2018

  213. [221]

    Mingzhu Zhang, Ruiping Yin, Zhen Yang, Yipeng Wang, and Kan Li. 2023. Advances and challenges of multi-task learning method in recommender system: a survey. arXiv preprint arXiv:2305.13843 (2023)

    2023 arXiv

  214. [222]

    Qingfu Zhang and Hui Li. 2007. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11, 6 (2007), 712–731

    2007

  215. [223]

    Qingfu Zhang, Wudong Liu, Edward Tsang, and Botond Virginas. 2009. Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Transactions on Evolutionary Computation 14, 3 (2009), 456–474

    2009

  216. [224]

    Qi Zhang, Peiyao Xiao, Kaiyi Ji, and Shaofeng Zou. 2025. MGDA Converges under Generalized Smoothness, Provably. In International Conference on Learning Representations

    2025

  217. [225]

    Richard Zhang and Daniel Golovin. 2020. Random hypervolume scalarizations for provable multi-objective black box optimization. In International Conference on Machine Learning

    2020

  218. [226]

    Xiaoyuan Zhang, Genghui Li, Xi Lin, Yichi Zhang, Yifan Chen, and Qingfu Zhang. 2024. Gliding over the Pareto Front with Uniform Designs. In Conference on Neural Information Processing Systems

    2024

  219. [227]

    Xiaoyuan Zhang, Xi Lin, Bo Xue, Yifan Chen, and Qingfu Zhang. 2023. Hypervolume maximization: A geometric view of Pareto set learning. In Conference on Neural Information Processing Systems

    2023

  220. [228]

    Xiaoyuan Zhang, Xi Lin, and Qingfu Zhang. 2024. PMGDA: A Preference-based Multiple Gradient Descent Algorithm. IEEE Transactions on Emerging Topics in Computational Intelligence (2024)

    2024

  221. [229]

    Xiaoyuan Zhang, Liang Zhao, Yingying Yu, Xi Lin, Yifan Chen, Han Zhao, and Qingfu Zhang. 2024. LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch. In Conference on Neural Information Processing Systems

    2024

  222. [230]

    Yu Zhang and Qiang Yang. 2022. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering 34, 12 (2022), 5586–5609

    2022

  223. [231]

    Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S Du, and Jason D Lee. 2024. Optimal multi-distribution learning. In Annual Conference on Learning Theory

    2024

  224. [232]

    Zhe Zhao, Pengkun Wang, HaiBin Wen, Wei Xu, Song Lai, Qingfu Zhang, and Yang Wang. 2024. Two Fists, One Heart: Multi-Objective Optimization Based Strategy Fusion for Long-tailed Learning. In International Conference on Machine Learning

    2024

  225. [233]

    Yong Zheng and David Xuejun Wang. 2022. A survey of recommender systems with multi-objective optimization. Neurocomputing 474 (2022), 141–153

    2022

  226. [234]

    Yifan Zhong, Chengdong Ma, Xiaoyuan Zhang, Ziran Yang, Qingfu Zhang, Siyuan Qi, and Yaodong Yang. 2024. Panacea: Pareto Alignment via Preference Adaptation for LLMs. In Conference on Neural Information Processing Systems

    2024

  227. [235]

    Shiji Zhou, Wenpeng Zhang, Jiyan Jiang, Wenliang Zhong, Jinjie Gu, and Wenwu Zhu. 2022. On the convergence of stochastic multi-objective gradient manipulation and beyond. In Conference on Neural Information Processing Systems

    2022

  228. [236]

    Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, and Yu Qiao. 2024. Beyond one-preference-fits-all alignment: Multi-objective direct preference optimization. In Findings of Annual Meeting of the Association for Computational Linguistics

    2024

  229. [237]

    Zhipeng Zhou, Liu Liu, Peilin Zhao, and Wei Gong. 2024. Pareto Deep Long-Tailed Recognition: A Conflict-Averse Solution. In International Conference on Learning Representations

    2024

  230. [238]

    Baiting Zhu, Meihua Dang, and Aditya Grover. 2023. Scaling Pareto-efficient decision making via offline multi-objective RL. In International Conference on Learning Representations

    2023

  231. [239]

    Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Tingjun Hou, and Jian Wu. 2023. Sample-efficient multi-objective molecular optimization with GFlowNets. In Conference on Neural Information Processing Systems

    2023

  232. [240]

    Eckart Zitzler and Lothar Thiele. 1998. Multiobjective optimization using evolutionary algorithms—a comparative case study. In International Conference on Parallel Problem Solving From Nature

    1998

  233. [241]

    Marcela Zuluaga, Andreas Krause, et al. 2016. e-PAL: An active learning approach to the multi-objective optimization problem. Journal of Machine Learning Research 17, 104 (2016), 1–32

    2016

  234. [242]

    Marcela Zuluaga, Guillaume Sergent, Andreas Krause, and Markus Püschel. 2013. Active learning for multi-objective optimization. In International Conference on Machine Learning . Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009 Manuscript submitted to ACM

    2013

  235. [2024]

    In Conference on Neural Information Processing Systems

    HelpSteer2: Open-source dataset for training top-performing reward models. In Conference on Neural Information Processing Systems

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.