M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
CoRR(2024)
摘要
When a neural network parameterized loss function consists of many terms, the
combinatorial choice of weight multipliers during the optimization process
forms a challenging problem. To address this, we proposed a probabilistic
graphical model (PGM) for the joint model parameter and multiplier evolution
process, with a hypervolume based likelihood that promotes multi-objective
descent of each loss term. The corresponding parameter and multiplier
estimation as a sequential decision process is then cast into an optimal
control problem, where the multi-objective descent goal is dispatched
hierarchically into a series of constraint optimization sub-problems. The
sub-problem constraint automatically adapts itself according to Pareto
dominance and serves as the setpoint for the low level multiplier controller to
schedule loss landscapes via output feedback of each loss term. Our method is
multiplier-free and operates at the timescale of epochs, thus saves tremendous
computational resources compared to full training cycle multiplier tuning. We
applied it to domain invariant variational auto-encoding with 6 loss terms on
the PACS domain generalization task, and observed robust performance across a
range of controller hyperparameters, as well as different multiplier initial
conditions, outperforming other multiplier scheduling methods. We offered
modular implementation of our method, admitting custom definition of many loss
terms for applying our multi-objective hierarchical output feedback training
scheme to other deep learning fields.
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