Real-world applications of deep learning often have to contend with objectives beyond predictive performance, i.e., more than one equally important and competing objective or criterion. Examples include cost functions pertaining to invariance (e.g., to photometric or geometric variations), semantic independence (e.g., to age or race for face recognition systems), privacy (e.g., mitigating leakage of sensitive information), algorithmic fairness (e.g., demographic parity), generalization across multiple domains, computational complexity (FLOPs, compactness), to name a few. In such applications, achieving a single solution that simultaneously optimizes all objectives is no longer feasible; instead, finding a set of solutions that are representative in describing the trade-off among objectives becomes the goal. Multiple approaches have been developed for such problems, including simple scalarization and population-based methods.
This tutorial aims to provide a comprehensive introduction to fundamentals, recent advances, and applications of multi-objective optimization (MOO), followed by hands-on coding examples. Some emerging applications of MOO include,
|09:20am-10:00am||Introduction to Multi-Objective Optimization||Kalyanmoy Deb|
|10:00am-10:30am||MOEA/D: Multi-Objective Evolutionary Algorithm Based on Decomposition||Qingfu Zhang|
|10:50am-11:15am||Multi-Objective Optimization for Multi-Task Learning||Xi Lin|
|11:15am-11:40am||Designing CNNs for Secure Inference||Vishnu Boddeti|
|11:40am-12:05pm||Multi-Objective Neural Architecture Search||Zhichao Lu|
|12:05am-12:30pm||Hands-On Session Notebook||Zhichao Lu|