Multi-objective learning (MOL) often arises in emerging machine learning problems when
multiple learning criteria or tasks need to be addressed. Recent works have developed …

Fine-grained control over large language models (LLMs) remains a significant challenge,
hindering their adaptability to diverse user needs. While Reinforcement Learning from …

Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …

Multi-objective optimization (MOO) has become an influential framework in many machine
learning problems with multiple objectives such as learning with multiple criteria and multi …

Multi-scenario and multi-task recommendation can use various feedback behaviors of users
in different scenarios to learn users' preferences and then make recommendations, which …

Model merging offers an effective strategy to combine the strengths of multiple finetuned
models into a unified model that preserves the specialized capabilities of each. Existing …

Meta-learning, or" learning to learn", enables machines to acquire general priors with
minimal supervision and rapidly adapt to new tasks. Unlike traditional AI methods that …

Multiobjective optimization problems (MOPs) are prevalent in machine learning, with
applications in multi-task learning, learning under fairness or robustness constraints, etc …

Multi-objective optimization problems can be found in many real-world applications, where
the objectives often conflict each other and cannot be optimized by a single solution. In the …

By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared
knowledge across tasks, resulting in improved data efficiency and generalization …