A multi-objective/multi-task learning framework induced by pareto stationarity

M Momma, C Dong, J Liu - International Conference on …, 2022 - proceedings.mlr.press
Multi-objective optimization (MOO) and multi-task learning (MTL) have gained much
popularity with prevalent use cases such as production model development of regression …

Federated multi-objective learning

H Yang, Z Liu, J Liu, C Dong… - Advances in neural …, 2023 - proceedings.neurips.cc
In recent years, multi-objective optimization (MOO) emerges as a foundational problem
underpinning many multi-agent multi-task learning applications. However, existing …

Multi-label learning to rank through multi-objective optimization

D Mahapatra, C Dong, Y Chen, M Momma - Proceedings of the 29th …, 2023 - dl.acm.org
Learning to Rank (LTR) technique is ubiquitous in Information Retrieval systems, especially
in search ranking applications. The relevance labels used to train ranking models are often …

An Innovative Hybrid-Box tool for Ranking Preferences using Multi-objective Evolutionary Gradient Strategies

OAS Ibrahim - 2024 - researchsquare.com
Optimizing data features plays a crucial role in simplifying the process of selecting instances
and analyzing datasets, especially when dealing with ranking problems. In scenarios such …

[CARTE][B] Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom

G Allen - 2021 - search.proquest.com
Bicycle design has not changed for a long time, as they are well-crafted for those that
possess the skills to ride, ie, adults. Those learning to ride, however, often need additional …