The paper introduces a framework for learning feature transformations on tabular data that addresses three limitations of prior generative methods: neglect of hierarchical feature relationships, bias from order-sensitive embeddings, and reliance on gradient-based search. It combines a permutation‑invariant hierarchical module using self‑attention pooling to capture interactions across features, operations, and abstraction levels, with a policy‑guided multi‑objective reinforcement learning strategy that starts from strong seeds and optimizes both predictive accuracy and transformation efficiency. Experiments on diverse tabular benchmarks show the approach outperforms strong baselines, and the authors provide public code and data.
arXiv:2607. 23286v1 Announce Type: new Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space.
The paper introduces TOPOFE, a framework that treats automatic feature engineering for tabular data as a graph-structured multi-island evolutionary search. Each island explores a semantically coherent family of transformations using LLM-guided mutation and crossover, while a Prompt Adaptation Memory steers proposals based on accept/reject feedback. TOPOFE dynamically learns a directed topology graph to coordinate cross-island transfer, enabling the discovery of compositional feature programs that outperform state‑of‑the‑art methods on 29 datasets and produce lower redundancy and higher representational coverage.
arXiv:2602. 11626v3 Announce Type: replace-cross Abstract: Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and evolving geometries requiring accurate, geometry-aware predictions at arbitrary spatial locations.
arXiv:2604. 00086v2 Announce Type: replace-cross Abstract: The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks.
arXiv:2510. 18315v2 Announce Type: replace-cross Abstract: We investigate how embedding dimension affects the emergence of an internal "world model" in a transformer trained with reinforcement learning to perform bubble-sort-style adjacent swaps.
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
arXiv:2607. 24804v2 Announce Type: replace-cross Abstract: Recommendation systems have undergone significant transformations in the past years.
arXiv:2608. 04348v1 Announce Type: cross Abstract: Multimodal learning of images and tabular data is often impaired by ineffective representations, resulting in redundancy, dispersion, and generalization problems.
arXiv:2505.09716v3 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) generalisation is considered a hallmark of human and animal intelligence. To achieve OOD through composition, a sys...
arXiv:2607. 27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance.
arXiv:2501. 14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL).
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.