Sparse Oblique Rule Boosting for Simpler Additive Rule Ensembles
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
arXiv:2603. 07606v2 Announce Type: replace Abstract: Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust.
Neural Symbolic Regression (NSR) uses neural networks as functional preconditioners to learn smooth, noise‑robust approximations of target functions in an interaction‑aware nonlinear feature space. A subsequent LASSO step extracts sparse, interpretable closed‑form expressions, while distributed hyperparameter optimization with Ray Tune and ASHA scheduling improves predictive accuracy and symbolic fidelity. Experiments on the Nguyen benchmark demonstrate that NSR outperforms SINDy and untuned neural baselines in RMSE, noise robustness, and out‑of‑distribution generalization, with ablation studies highlighting the importance of feature interactions, neural depth, and tuning strategies.
The paper introduces mixed integer linear programming (MILP) formulations for training sparse disjunctive normal form (DNF) networks, which are small networks with a conjunctive hidden layer and a disjunctive output layer. By fixing the network structure and optimizing weights, the authors achieve globally optimized rule sets that balance misclassification costs against sparsity. Their enhanced formulation reduces integer decision variables and redundant constraints, and a combined initialization and dropout approach breaks weight symmetries to promote diverse, stable rule representations, leading to improved computational efficiency and competitive accuracy on synthetic and UCI datasets.
SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.
The paper introduces a new algorithmic framework for learning Boolean concept classes under logconcave distributions, focusing on multi‑index models. It leverages a boosting approach based on weak partial learners, overcoming the need for distribution‑free weak learners by using a recursive moment‑based partitioning procedure that aligns low‑degree moments of conditioned distributions with the original logconcave distribution. This yields faster robust learners for fundamental concept classes such as functions of halfspaces and multiclass linear classifiers, and includes an efficient method for detecting moment discrepancies that may be useful beyond this work.