A collection of Awesome papers on Pareto Front Learning, Pareto Multi Task Learning, Multiple Gradient Descent methods to solve Multi-Objective Optimization, Multi-Task Learning as Multi-Objective Optimization, and Optimization in Pareto Set.

• Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach [ICLR 2023] [code]
• Independent Component Alignment for Multi-Task Learning [CVPR 2023] [code]

• Stochastic Multiple Target Sampling Gradient Descent [NeurIPS 2022] [code]
• Multi-Task Learning as a Bargaining Game [ICML 2022] [code]
• Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning [TMLR 2022] [code]
• A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity [ICML 2022]

• Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent [NeurIPS 2021 (Spotlight)] [code]
• Conflict-Averse Gradient Descent for Multi-task Learning [NeurIPS 2021] [code]
• Towards Impartial Multi-task Learning [ICLR 2021] [code]
• Controllable Pareto Multi-Task Learning [Arxiv 2021] [code]
• Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models [ICLR 2021] [code]

• Multi-Task Learning with User Preferences Gradient Descent with Controlled Ascent in Pareto Optimization [ICML 2020] [code]
• Gradient Surgery for Multi-Task Learning [NeurIPS 2020] [code]

• Pareto Multi-Task Learning [NeurIPS 2019] [code]

• Multi-Task Learning as Multi-Objective Optimization [NIPS 2018] [code]

• A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications [Arxiv 2023] [code]
• Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models [ICML 2023] [code]
• Improving Pareto Front Learning via Multi-Sample Hypernetworks [AAAI 2023] [code]

• Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization [ICLR 2022][code]
• Pareto Set Learning for Expensive Multi-Objective Optimization [NeurIPS 2022][code]

• Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization [Arxiv 2021] [code]
• Self-Evolutionary Optimization for Pareto Front Learning [Arxiv 2021]
• Learning the Pareto Front with Hypernetworks [ICLR 2021][code]
• Scalable Pareto Front Approximation for Deep Multi-Objective Learning [ICDM 2021] [code]

• Effcient Continuous Pareto Exploration in Multi-Task Learning [ICML 2020] [code]

• Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set [UAI 2022][code]
• Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing [Arxiv 2021]