Many biochemical applications such as molecular property prediction require
models to generalize beyond their training domains (environments). Moreover,
natural environments in these tasks are structured, defined by complex
descriptors such as molecular scaffolds or protein families. Therefore, most
environments are either never seen during training, or contain only a single
training example. To address these challenges, we propose a new regret
minimization (RGM) algorithm and its extension for structured environments. RGM
builds from invariant risk minimization (IRM) by recasting simultaneous
optimality condition in terms of predictive regret, finding a representation
that enables the predictor to compete against an oracle with hindsight access
to held-out environments. The structured extension adaptively highlights
variation due to complex environments via specialized domain perturbations. We
evaluate our method on multiple applications: molecular property prediction,
protein homology and stability prediction and show that RGM significantly
outperforms previous state-of-the-art baselines.

本文提出了一种新的后悔最小化算法和其在结构化环境中的扩展，并在分子属性预测，蛋白质同源性和稳定性预测等多个应用中表现出了显著的性能优势。

跨结构生物医学领域实现预测不变性

Enforcing Predictive Invariance across Structured Biomedical Domains

Humans learn a predictive model of the world and use this model to reason
about future events and the consequences of actions. In contrast to most
machine predictors, we exhibit an impressive ability to generalize to unseen
scenarios and reason intelligently in these settings. One important aspect of
this ability is physical intuition(Lake et al., 2016). In this work, we explore
the potential of unsupervised learning to find features that promote better
generalization to settings outside the supervised training distribution. Our
task is predicting the stability of towers of square blocks. We demonstrate
that an unsupervised model, trained to predict future frames of a video
sequence of stable and unstable block configurations, can yield features that
support extrapolating stability prediction to blocks configurations outside the
training set distribution

本文探讨了无监督学习发现特征以提高预测模型泛化性能的潜力，并利用视频序列预测方块塔的稳定性，展示了模型训练得出的特征能够支持对超出训练集分布的方块配置进行稳定性预测。