Title: Noise$^+$2Noise: Co-taught De-noising Autoencoders for Time-Series Data. Abstract: We consider the task of learning to recover clean signals given only access to noisy data. Recent work in computer vision has addressed this problem in the context of images using denoising autoencoders (DAEs).  However, to date DAEs for learning from noisy data have not been explored in the context of time-series data.  DAEs for denoising images often rely on assumptions unlikely to hold in the context of time series, \textit{e.g.}, multiple noisy samples of the same example. Here, we adapt DAEs to cleaning time-series data with noisy samples only. To recover the clean target signal when only given access to noisy target data,  we leverage a noise-free auxiliary time-series signal that is related to the target signal. In addition to leveraging the relationship between the target signal and auxiliary signal, we iteratively filter and learn from clean samples using an approach based on co-teaching. Applied to the task of recovering carbohydrate values for blood glucose management, our approach reduces noise (MSE) in patient-reported carbohydrates from 72$g^2$ (95\% CI: 54,93) to 18$g^2$ (13,25), outperforming the best baseline (MSE = 33$g^2$ (27,43)). We demonstrate strong time-series denoising performance, extending the applicability of DAEs to a previously under-explored setting.