Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms
Authors: Vikas Garg, Tamar Pichkhadze
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe the results of our experiments on two real datasets. We first compare the performance of our methods with the state-of-the-art Online Step Algorithm (OSA) [24] that also provides theoretical guarantees for first order Markov decoding under latency constraints. ... We experimented with the Glycerol Tra SH genome data [35] pertaining to M. tuberculosis transposon mutants. |
| Researcher Affiliation | Academia | Vikas K. Garg MIT EMAIL Tamar Pichkhadze MIT EMAIL |
| Pseudocode | No | We outline an efficient procedure, underlying Theorem 2, in the supplementary material. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We experimented with the Glycerol Tra SH genome data [35] pertaining to M. tuberculosis transposon mutants. |
| Dataset Splits | Yes | The corpus is not divided into separate train and test sets. Therefore, we formed 5 random partitions each having 80% train and 20% test sentences. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | We used the parameter settings suggested by [35] for decoding with an HMM. ... We varied the OSA hyperparameter λ {10 4, 10 1, . . . , 104} under both the entropy and the expected classification error measures suggested by [24] to tune for L (as noted in [24], large values of λ penalize latency). |