Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

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 …

Multi-objective optimization can be found in many real-world applications where some
conflicting objectives can not be optimized by a single solution. Existing optimization …

We study a federated version of multi-objective optimization (MOO), where a single model is
trained to optimize multiple objective functions. MOO has been extensively studied in the …

Jointly learning multiple tasks with a unified model can improve accuracy and data
efficiency, but it faces the challenge of task interference, where optimizing one task objective …

Multitask learning is a widely used paradigm for training models on diverse tasks, with
applications ranging from graph neural networks to language model fine-tuning. Since tasks …

Accurate face detection and subsequent localization of facial landmarks are mandatory
steps in many computer vision applications, such as emotion recognition, age estimation …

Prevalent nighttime person re-identification (ReID) methods typically combine image
relighting and ReID networks in a sequential manner. However, their performance …

We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost
the primary task performance which we focus on, while preserving a single task inference …

Data availability across domains often follows a long-tail distribution: a few domains have
abundant data, while most face dat. a scarcity. This imbalance poses challenges in training …