Nonconvex projected gradient descent for noisy inductive matrix completion achieves linear convergence and order-optimal error at sample complexity scaling with side-information dimension a instead of ambient dimension n.
Handbook of Reinforcement Learning and Control , pages=
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A sharp threshold at zero reach-weighted contingent action capacity governs whether self-play RL collapses to a deterministic exploitation attractor under asymmetric perturbations.
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Sample efficient inductive matrix completion with noise and inexact side information
Nonconvex projected gradient descent for noisy inductive matrix completion achieves linear convergence and order-optimal error at sample complexity scaling with side-information dimension a instead of ambient dimension n.
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A Structural Threshold in Decision Capacity Governs Collapse in Self-Play Reinforcement Learning
A sharp threshold at zero reach-weighted contingent action capacity governs whether self-play RL collapses to a deterministic exploitation attractor under asymmetric perturbations.