Echoing recent calls to counter reliability and robustness concerns in machine learning via multiverse analysis, we present PRESTO, a principled framework for mapping the multiverse of machine-learning models that rely on latent representations. Although such models enjoy widespread adoption, the variability in their embeddings remains poorly understood, resulting in unnecessary complexity and untrustworthy representations. Our framework uses persistent homology to characterize the latent spaces arising from different combinations of diverse machine-learning methods, (hyper)parameter configurations, and datasets, allowing us to measure their pairwise (dis)similarity and statistically reason about their distributions. As we demonstrate both theoretically and empirically, our pipeline preserves desirable properties of collections of latent representations, and it can be leveraged to perform sensitivity analysis, detect anomalous embeddings, or efficiently and effectively navigate hyperparameter search spaces.

翻译：针对近期通过多重宇宙分析应对机器学习可靠性和鲁棒性问题的呼声，我们提出PRESTO框架——一个用于映射依赖潜在表示的机器学习模型多重宇宙的原则性框架。尽管这类模型已得到广泛应用，但其嵌入的变异性仍缺乏深入理解，导致不必要的复杂性和不可靠的表示。我们的框架利用持久同调来刻画不同机器学习方法、(超)参数配置和数据集组合所产生的潜在空间，从而能够度量它们的成对(非)相似性，并对其分布进行统计推理。通过理论与实证双重验证，我们的处理流程既保留了潜在表示集合的理想性质，又可应用于敏感性分析、异常嵌入检测，以及高效探索超参数搜索空间。