A new framework, MultiPruneEnsemble, is proposed, which integrates the diversity of pruning with the flexibility of gradient-based optimization, and indicates that integrating multiple decision tree pruning by gradient-based weighting is a feasible way to improve the accuracy and robustness of learning.
This work proposes the Multi-Branch Neural Decision Tree with Adaptive Pruning (MBNDT), a single axis-aligned tree trained end-to-end with differentiable multi-way splits that achieves the best average rank and mean balanced accuracy among depth-constrained single-tree baselines.
H. Park, Jeonghoon Choi, Juseong Kim et al.· 0 citations
Conventional decision tree algorithms produce effective, transparent models that can be audited, communicated, and deployed independently of the training data, but require learning every new task from scratch. In contrast, tabular foundation models demonstrate that meta-learning from a synthetic prior distribution enab...
EvoRank is presented, an open autonomous ranking engineer: an LLM-guided evolutionary loop that discovers complete Learning-to-Rank pipelines (features, models, losses, ensembles) for multi-objective e-commerce search and is released as a headroom gate that predicts, before any LLM spend, whether the loop will pay off.
Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-model post-training. Recent trends suggest that multi-teacher on-policy distillation (MOPD) outperforms conventional off-policy methods. However, despite the higher inferen...
Roy Xie, Dan Friedman, Feng Nan et al.· 0 citations
Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors, significantly reduces the split search space for classification tasks while preserving predictive performance.
WOCFs complement existing methods such as CART (Classification and Regression Trees), optimal classification trees, random forests and XGBoost and provide nontrivial improvement in interpretability and usability of the trained model in terms of counterfactual explanation.
Víctor Blanco, Alberto Japón, Justo Puerto et al.· INFORMS journal on computing· 0 citations
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