Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Vikas Garg, Tamar Pichkhadze


We resolve the fundamental problem of online decoding with general nth order ergodic Markov chain models. Specifically, we provide deterministic and randomized algorithms whose performance is close to that of the optimal offline algorithm even when latency is small. Our algorithms admit efficient implementation via dynamic programs, and readily extend to (adversarial) non-stationary or time-varying settings. We also establish lower bounds for online methods under latency constraints in both deterministic and randomized settings, and show that no online algorithm can perform significantly better than our algorithms. To our knowledge, our work is the first to analyze general Markov chain decoding under hard constraints on latency. We provide strong empirical evidence to illustrate the potential impact of our work in applications such as gene sequencing.