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Found 5 papers, 1 papers with code
Bayesian Decision Trees Inspired from Evolutionary Algorithms
Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) because they can handle complex and uncertain data.
Parallel Approaches to Accelerate Bayesian Decision Trees
None-the-less, we propose two methods for exploiting parallelism in the MCMC: in the first, we replace the MCMC with another numerical Bayesian approach, the Sequential Monte Carlo (SMC) sampler, which has the appealing property that it is an inherently parallel algorithm; in the second, we consider data partitioning.
Single MCMC Chain Parallelisation on Decision Trees
Decision trees are highly famous in machine learning and usually acquire state-of-the-art performance.
Threshold-based Network Structural Dynamics
In particular, in each discrete round $t$, each pair of nodes $u$ and $v$ that are allowed to communicate by the scheduler, computes a value $\mathcal{E}(u, v)$ (the potential of the pair) as a function of the local structure of the network at round $t$ around the two nodes.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
Crystal Structure Prediction via Oblivious Local Search
In addition, due to the chemical expertise involved in the parameter-tuning, these approaches can be {\em biased} towards previously-known crystal structures.
Computational Engineering, Finance, and Science
