arXiv Machine Learning September 10, 2026 By Karish Grover, Theodore Vasiloudis, Han Xie, Sixing Lu, Xiang Song, Christos Faloutsos

Feedback Control for Multi-Objective Graph Self-Supervision

Read the original on arXiv Machine Learning →

The paper introduces ControlG, a control‑theoretic framework for coordinating multi‑objective graph self‑supervised learning. It treats objective coordination as a temporal allocation problem, estimating each objective’s difficulty and antagonism, planning budgets with a Pareto‑aware log‑hypervolume planner, and scheduling updates via a PID controller. Experiments on nine datasets show that ControlG consistently outperforms state‑of‑the‑art baselines and provides an auditable schedule revealing which objectives drive learning.

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