Pareto Optimization

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Learning Pareto Stationary Fronts via Single-Pass Backpropagation
Oct 5, 2026Elina Rojin Celik, Marcos Medeiros Raimundo, Isabel ValeraDeep Learning Optimization Multi-Objective Optimization
Department of Computer Science Saarland University, Germany · Instituto de Computação Universidade Estadual de Campinas, Brazil
ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front
Sep 7, 2026Stephanie Holly, Sepp Hochreiter, Werner ZellingerMulti-Objective Optimization Pareto Optimization
LIT AI Lab and Institute for Machine Learning JKU Linz, Austria
Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation
Aug 5, 2026Bo Xue, Zhi Hong, Jiayi Li +3Multi-Armed Bandits LLM Evaluation
City University of Hong Kong · South China University of Technology · Zhejiang University
Top- $k$ Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection
Jul 28, 2026Nicolas Gutowski, Fabien Chhel, Alexandre Letard +1Multi-Armed Bandits Combinatorial Multi-Armed Bandits
Université d’Angers, LERIA, SFR MATHSTIC, Angers, France · ESEO, ERIS, Angers, France · AlphaEdge, Saint-Barthélémy-d’Anjou, France +1
Fair Division with Strictly Increasing Valuations: A Tight Threshold for Two-Agent EF1 and PO
Jul 25, 2026Nicholas TehSocial Choice Theory Fair Resource Allocation
University of Oxford, UK
Representing the Non-dominated Set of Multi-objective Network Problems by Supported Non-dominated Points
Jul 13, 2026David Könen, Lara Löhken, Michael StiglmayrCombinatorial Optimization Multi-Objective Optimization
University of Wuppertal, School of Mathematics and Natural Sciences, Optimization Group, Gaußstraße 20, 42103 Wuppertal, Germany · Corresponding author
Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning
Jun 24, 2026Aniruddha Joshi, Niklas Lauffer, Sanjit SeshiaMulti-Objective Reinforcement Learning Multi-Objective Optimization
University of California Berkeley, USA.
Selective Ensemble Based on Preference-Directed Multi-Objective Bandits
Jun 20, 2026Lanjihong Ma, Zhen-Yu Zhang, Masashi Sugiyama +1Multi-Armed Bandits Ensemble Learning
Department of Computer Science, Zhejiang Gongshang University, China · Center for Advanced Intelligence Project, RIKEN, Japan · Graduate School of Frontier Sciences, The University of Tokyo, Japan +1
Simultaneously Efficient Allocation of Indivisible Items Across Multiple Dimensions
Jun 19, 2026Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin SanpuiCombinatorial Optimization Approximation Algorithms
Chuo University, Tokyo, Japan · Indian Institute of Technology Kharagpur, Kharagpur, India
Interactive Pareto navigation for deep multi-task learning
Jun 17, 2026Augustina C. Amakor, Konstantin Sonntag, Sebastian PeitzMulti-Task Learning Pareto Optimization
Department of Computer Science, TU Dortmund, Dortmund, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
Jun 17, 2026Lennert Saerens, Bram Silue, Eleni Litsa +2Thompson Sampling Multi-Armed Bandits
Artificial Intelligence Lab, Departement of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium · imec, Leuven, Belgium
Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization
Jun 15, 2026Junyi Li, Xiaowei Qian, Yingyi Zhang +6Multi-Objective Reinforcement Learning Multi-Objective Language Model Alignment
Department of Data Science, City University of Hong Kong, Hong Kong, China · Zhejiang University of Technology, Hangzhou, China · Huawei Technologies Noah’s Ark Lab, Hong Kong, China
Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning
Jun 13, 2026Yiyi Zhu, Yaolin Wen, Xiang Xia +6Surrogate-Assisted Optimization Multi-Objective Optimization
Shanghai Institute of AI for Education, and School of Computer Science and Technology, East China Normal University, Shanghai, China · Ant Group, Hangzhou, China · Shanghai Innovation Institute, Shanghai, China
Post-AGI Economies: Superposition and the Second Fundamental Theorem of Welfare Economics
Jun 6, 2026Elija PerrierAI Agent Governance Pareto Optimization
Centre for Quantum Software & Information, University of Technology Sydney, Sydney, Australia
SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
May 20, 2026Liuyuan Jiang, Chentong Huang, Lisha ChenAdaptive Sampling Multi-Objective Optimization
Department of Electrical and Computer Engineering University of Rochester, Rochester, NY 14627
MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
May 19, 2026Md Mehrab Tanjim, Jayakumar Subramanian, Xiang Chen +6LLM Agent Skill Learning Multi-Objective Optimization
Adobe Research
A Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions
May 19, 2026Nguyen Viet Hoang, Dung D. Le, Tran Ngoc ThangHypernetworks Multi-Objective Optimization
Nonsmooth Set-Gradient Ascent to the Pareto Front via Layered Hypervolume and Magnitude Indicators
May 13, 2026Michael T. M. EmmerichMulti-Objective Optimization Pareto Optimization
Faculty of Information Technology, University of Jyväskylä, Finland
Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
May 12, 2026Peipei Xu, SiYuan Ma, Yaohua Liu +4Large Language Model-Guided Optimization Constrained Optimization
University of Shanghai for Science and Technology · The Hong Kong University of Science and Technology
Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems
May 11, 2026Mieke Wilms, Christoph HeitzAlgorithmic Fairness Group Fairness
Zurich University of Applied Sciences / University of Zurich, Switzerland · Zurich University of Applied Sciences, Switzerland
From Feasible to Practical: Pareto-Optimal Synthesis Planning
May 8, 2026Friedrich Hastedt, Dongda Zhang, Antonio del Rio ChanonaMulti-Objective Optimization Pareto Optimization
Department of Chemical Engineering, Imperial College London, UK · Department of Chemical Engineering, University of Manchester, UK
CoAction: Cross-task Correlation-aware Pareto Set Learning
May 3, 2026Xinyue Chen, Yingxuan Liang, Yiqin Huang +3Multi-Task Learning Multi-Task Optimization
Guangdong University of Technology, Guangzhou, China · Xiamen University, Xiamen, China
POCA: Pareto-Optimal Curriculum Alignment for Visual Text Generation
Apr 27, 2026Yaohou Fan, Qingzhong Wang, Yongsong Huang +3Multi-Objective Reinforcement Learning RL for Image Generation
Tohoku University · Amazon Web Services
The Magnitude of Dominated Sets: A Pareto Compliant Indicator Grounded in Metric Geometry
Apr 20, 2026Michael T. M. EmmerichMulti-Objective Optimization Pareto Optimization
Faculty of Information Technology, University of Jyväskylä, Finland

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Learning Pareto Stationary Fronts via Single-Pass Backpropagation

Elina Rojin Celik, Marcos Medeiros Raimundo, Isabel Valera

Department of Computer Science Saarland University, Germany · Instituto de Computação Universidade Estadual de Campinas, Brazil

Deep Learning Optimization Multi-Objective Optimization Pareto Optimization

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We propose MOSEL (Multi-Objective Stackelberg Efficient Learning), a framework for a posteriori multi-objective optimization (MOO) in deep neural networks that recovers a full front of Pareto stationary solutions at the computational cost of standard single-objective training. MOSEL reformulates the problem as a bilevel optimization problem that leverages network modularity to decouple representation learning from objective-preference alignment. Casting the bilevel optimization problem as a Stackelberg game enables solving the original a posteriori MOO problem in a single forward-backward pass. As a result, MOSEL matches the time and memory efficiency of standard single-objective training while enabling scalable Pareto stationary front learning. Empirically, MOSEL uncovers diverse and optimal Pareto frontiers in strongly conflicting settings (e.g., fairness-accuracy). Remarkably, even in weakly conflicting regimes such as multi-task learning, it consistently converges to solutions closer to the utopia point, outperforming both standard single-objective training and specialized multi-task learning methods. These results highlight the broader potential of a posteriori MOO learning as a pathway to efficiently learn more diverse and robust representations, ultimately improving generalization.

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