Leveraging Time Irreversibility with Order-Contrastive Pre-training

• URL: http://arxiv.org/abs/2111.02599v1
• Date: Thu, 4 Nov 2021 02:56:52 GMT
• Title: Leveraging Time Irreversibility with Order-Contrastive Pre-training
• Authors: Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David Sontag
• Abstract summary: We explore an "order-contrastive" method for self-supervised pre-training on longitudinal data. We prove a finite-sample guarantee for the downstream error of a representation learned with order-contrastive pre-training. Our results indicate that pre-training methods designed for particular classes of distributions and downstream tasks can improve the performance of self-supervised learning.
• Score: 3.1848820580333737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques. To overcome the difficulties posed by a lack of labeled data, we explore an "order-contrastive" method for self-supervised pre-training on longitudinal data. We sample pairs of time segments, switch the order for half of them, and train a model to predict whether a given pair is in the correct order. Intuitively, the ordering task allows the model to attend to the least time-reversible features (for example, features that indicate progression of a chronic disease). The same features are often useful for downstream tasks of interest. To quantify this, we study a simple theoretical setting where we prove a finite-sample guarantee for the downstream error of a representation learned with order-contrastive pre-training. Empirically, in synthetic and longitudinal healthcare settings, we demonstrate the effectiveness of order-contrastive pre-training in the small-data regime over supervised learning and other self-supervised pre-training baselines. Our results indicate that pre-training methods designed for particular classes of distributions and downstream tasks can improve the performance of self-supervised learning.

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