SATzilla employs empirical hardness models for dynamic SAT instance algorithm selection, significantly improving solving efficiency.

Lin Xu, Frank Hutter, Holger H. Hoos et al.

Perseus: a randomized point-based value iteration method for large-scale POMDPs, improving efficiency with belief subset sampling

M. T. J. Spaan, N. Vlassis

Grounded Semantic Composition on Bishop: 59% correct referent selection.

P. Gorniak, D. Roy

SMOTE: Synthetic Minority Over-sampling Technique improves classifier performance on imbalanced datasets by generating synthetic minority samples, boosting AUC by over 0.07 in various experiments.

N. V. Chawla, K. W. Bowyer, L. O. Hall et al.