arXiv Machine Learning
Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
2d ago
LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
arXiv:2609.36396v1 Announce Type: cross Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical cha...
arXiv Machine Learning
Aug 4
Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection
arXiv:2511. 20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores.
arXiv Machine Learning
Jul 23
Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
arXiv:2508. 14268v2 Announce Type: replace-cross Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest.
arXiv Machine Learning
Aug 19
Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
Data-DPO is a target model‑oriented supervised fine‑tuning data selection method that uses one‑step probing of the target model to generate pairwise data preferences, trains a lightweight reward model to capture these preferences, and then selects a training subset by combining target‑model preference, external quality scores, and marginal diversity. Experiments on Vision‑Flan and LLaVA‑CoT demonstrate that Data‑DPO consistently outperforms existing data selection baselines across multiple data budgets and even surpasses full data training performance.
arXiv Machine Learning
Jun 2
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.
arXiv Machine Learning
Jul 14
Vertical Fusion: Condensing Internal Representations for Robust ViT Classification
arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.