Develops a McDiarmid-type concentration inequality for causal autoregressive processes that preserves sparsity to achieve O(1) variance proxies instead of O(N).
Large deviations for sums of partly dependent random variables.Random Structures & Algorithms, 24(3):234–248
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Matrix-Decoupled Concentration for Autoregressive Sequences: Dimension-Free Guarantees for Sparse Long-Context Rewards
Develops a McDiarmid-type concentration inequality for causal autoregressive processes that preserves sparsity to achieve O(1) variance proxies instead of O(N).