Zoom google
With the advancement of machine learning and deep learning, vector search becomes instrumental to many information retrieval systems, to search and find best matches to user queries based on their semantic similarities.These online services require the search architecture to be both effective with high accuracy and efficient with low latency and memory footprint, which existing work fails to offer. We develop, Zoom, a new vector search solution that collaboratively optimizes accuracy, latency and memory based on a multiview approach. (1) A ‘preview’ step generates a small set of good candidates, leveraging compressed vectors in memory for reduced footprint and fast lookup. (2) A ‘fullview’ step on SSDs reranks those candidates with their full-length vector, striking high accuracy. Our evaluation shows that, Zoom achieves an order of magnitude improvements on efficiency while attaining equal or higher accuracy, comparing with the state-of-the-art. …

MedImpute google
Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods. This issue becomes even more prominent for longitudinal data sets, where multiple instances of the same individual correspond to different observations in time. Standard imputation methods do not take into account patient specific information incorporated in multivariate panel data. We introduce the novel imputation algorithm MedImpute that addresses this problem, extending the flexible framework of OptImpute suggested by Bertsimas et al. (2018). Our algorithm provides imputations for data sets with missing continuous and categorical features, and we present the formulation and implement scalable first-order methods for a $K$-NN model. We test the performance of our algorithm on longitudinal data from the Framingham Heart Study when data are missing completely at random (MCAR). We demonstrate that MedImpute leads to significant improvements in both imputation accuracy and downstream model AUC compared to state-of-the-art methods. …

Interventional Robustness Score google
The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is of central importance for data efficient and robust use of neural networks. Various approaches aiming towards this goal have been proposed in the recent time — validating existing work is hence a crucial task to guide further development. Previous validation methods focused on shared information between generative factors and learned features. The effects of rare events or cumulative influences from multiple factors on encodings, however, remain uncaptured. Our experiments show that this already becomes noticeable in a simple, noise free dataset. This is why we introduce the interventional robustness score, which provides a quantitative evaluation of robustness in learned representations with respect to interventions on generative factors and changing nuisance factors. We show how this score can be estimated from labeled observational data, that may be confounded, and further provide an efficient algorithm that scales linearly in the dataset size. The benefits of our causally motivated framework are illustrated in extensive experiments. …

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