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

NeurIPS 2019 Vikas K. GargTamar Pichkhadze

We resolve the fundamental problem of online decoding with general $n^{th}$ 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... (read more)

PDF Abstract NeurIPS 2019 PDF NeurIPS 2019 Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet