Peek Search: Near-Optimal Online Markov Decoding

We resolve the fundamental problem of online decoding with ergodic Markov models. Specifically, we provide deterministic and randomized algorithms that are provably near-optimal under latency constraints with respect to the unconstrained offline optimal algorithm. Our algorithms admit efficient implementation via dynamic programs, and extend to (possibly adversarial) non-stationary or time-varying Markov settings as well. Moreover, we establish lower bounds in both deterministic and randomized settings subject to latency requirements, and prove that no online algorithm can perform significantly better than our algorithms.