Evaluation of Active Feature Acquisition Policies with Tabular Foundation Models
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:2610.34035v1 Announce Type: new Abstract: Off-policy evaluation (OPE) is essential for assessing decision policies before deployment when online experimentation is costly or unsafe. Accurate es...
arXiv:2510. 12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire.
arXiv:2606. 25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder.
The paper introduces Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients (NM-PPG), a method that relaxes the feature acquisition process to allow continuous, low‑variance policy gradients over the entire acquisition trajectory. It incorporates a straight‑through rollout that mimics discrete acquisitions during inference while enabling end‑to‑end training, and provides an average‑case upper bound on gradient variance to guide temperature sharpening. Experiments on synthetic and real datasets show that NM-PPG outperforms existing active feature acquisition baselines.
arXiv:2405. 19466v4 Announce Type: replace Abstract: We pose uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model, an area experiencing rapid innovation.
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.