arXiv Machine Learning
Jul 10

Bayesian Experimental Design via Score Matching

arXiv:2607. 08335v1 Announce Type: cross Abstract: Policy-based approaches to Bayesian experimental design (BED) allow the learning of deep policy networks that adaptively make intelligent design decisions based on previously collected data.

By Angus Phillips, Gavin Kerrigan, Tom Rainforth
arXiv Machine Learning
Jul 21

Deep Adaptive Bayesian Screening

arXiv:2607. 16927v1 Announce Type: cross Abstract: We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces.

By Jade Lejeune Herman, Arno Strouwen, Johan A. K. Suykens, Peter Goos
arXiv Statistics ML
6d ago

Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs

The paper introduces a learning-based surrogate approach for stochastic optimization problems where uncertainty depends on the decision, modeled via a nonparametric regression. It constructs a surrogate that embeds iteratively updated Jacobian estimates, using an adaptive random design that focuses sampling near the current iterate to achieve dimension‑independent convergence of the Jacobian estimates. The resulting learning‑based stochastic prox‑linear (L‑SPL) algorithm demonstrates nonasymptotic convergence rates and outperforms existing methods in sample efficiency and objective value in numerical experiments.

By Boyang Shen, Junyi Liu
arXiv Machine Learning
Jun 25

Efficient Adaptive Data Acquisition via Pretrained Belief Representations

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.

By Daolang Huang, Zhuoyue Huang, Conor Hassan, Luigi Acerbi, Samuel Kaski, Tom Rainforth