Jacobian Descent for Multi-Objective Optimization
Jacobian descent (JD) is a method for multi-objective optimization that uses a Jacobian matrix to find updates. AUPGrad is a specific aggregator designed to resolve conflicts between different objectives while maintaining key properties like non-conflicting, linear under scaling, and weighted. This framework allows for instance-wise risk minimization in neural networks.