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.
Proceedings of the national academy of sciences , volume=
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Proposes OPMD algorithm achieving accelerated O(1/n) rates for offline Nash equilibrium learning in alpha-potential games via reference-anchored data coverage.
KL regularization enables pessimism-free offline learning in general-sum games, recovering regularized Nash equilibria at accelerated rate O(1/n) via GANE and converging to coarse correlated equilibria at standard rate O(1/sqrt(n)+1/T) via GAMD.
Order isomorphisms transmit optimality across dynamic program formulations, with applications to Epstein-Zin preferences and numerical value function improvements.
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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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Fast Rates in $\alpha$-Potential Games via Regularized Mirror Descent
Proposes OPMD algorithm achieving accelerated O(1/n) rates for offline Nash equilibrium learning in alpha-potential games via reference-anchored data coverage.
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Pessimism-Free Offline Learning in General-Sum Games via KL Regularization
KL regularization enables pessimism-free offline learning in general-sum games, recovering regularized Nash equilibria at accelerated rate O(1/n) via GANE and converging to coarse correlated equilibria at standard rate O(1/sqrt(n)+1/T) via GAMD.
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Isomorphic Dynamic Programs
Order isomorphisms transmit optimality across dynamic program formulations, with applications to Epstein-Zin preferences and numerical value function improvements.