Low-rank graphs induce latents that form causal abstractions, with identifiability results and a practical objective enabling unsupervised learning of high-level SCMs from low-level measurements.
arXiv preprint arXiv:2503.14576 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Phi-Actor-Critic is a new method that steers multi-agent reinforcement learning toward Pareto-efficient correlated equilibria using regret minimization and Lagrangian selection.
Introduces α-fair HATRPO and HAPPO algorithms that integrate α-fairness into HATRL via a weighted advantage function while claiming to preserve convergence to Nash equilibria.
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Unsupervised Causal Abstractions Discovery
Low-rank graphs induce latents that form causal abstractions, with identifiability results and a practical objective enabling unsupervised learning of high-level SCMs from low-level measurements.