Prox-ITEM achieves the minimax-optimal distance-to-solution rate among span-based first-order methods for smooth strongly convex composite problems, with Prox-TMM as its stationary limit matching TMM rates.
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Musielak-Orlicz spaces enable bounding cumulants in uncertain supOU long-memory processes by state-dependent divergences on reversion and Levy measures, succeeding where Kullback-Leibler fails.
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An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit
Prox-ITEM achieves the minimax-optimal distance-to-solution rate among span-based first-order methods for smooth strongly convex composite problems, with Prox-TMM as its stationary limit matching TMM rates.
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A Musielak-Orlicz approach for modeling uncertainties in long-memory processes
Musielak-Orlicz spaces enable bounding cumulants in uncertain supOU long-memory processes by state-dependent divergences on reversion and Levy measures, succeeding where Kullback-Leibler fails.