Amazon Technologies, Inc.

Jpmorgan Chase Bank, N.a.

Sergul Aydore

Luca Melis

Krishnaram Kenthapadi

William Brown

Michael Kearns

Aaron Roth

Amaresh Ankit Siva

Baris Coskun

Sudhir Upadhyay

Tulasi Movva

An algorithm releases answers to very large numbers of statistical queries, e.g., k-way marginals, subject to differential privacy. The algorithm answers queries on a private dataset using simple perturbation, and then attempts to find a synthetic dataset that most closely matches the noisy answers. The algorithm uses a continuous relaxation of the synthetic dataset domain which makes the projection loss differentiable, and allows the use of efficient machine learning optimization techniques and tooling. Rather than answering all queries up front, the algorithm makes judicious use of a privacy budget by iteratively and adaptively finding queries for which relaxed synthetic data has high error, and then repeating the projection. The algorithm is effective across a range of parameters and datasets, especially when a privacy budget is small or a query class is large.

Generating relaxed synthetic data using adaptive projection

Systems, devices, and methods are provided for detecting anomalous events from categorical data using autoencoders. A system may receive a data set associated with actions requested within the computing environment, wherein the data set includes first categorical data indicative of anomalous activity in the computing environment. The system may train an autoencoder to reconstruct approximations of requests associated with the computing environment based on the received data set, wherein training the autoencoder includes using a beta divergence and a maximum mean discrepancy divergence. The trained system may receive a request to invoke an action within the computing environment, may generate a reconstruction of the request to invoke the action using the trained autoencoder, may determine a normalcy score based on a probability that the reconstruction of the request exists in the training data set, and, based on the calculated normalcy score, may determine whether requests indicate anomalous data.

Detecting anomalous events from categorical data using autoencoders

Systems and methods for machine-learning augmented application monitoring are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor and a memory, a method for machine-learning augmented application monitoring may include: (1) receiving data from a plurality of data sources; (2) storing the data in a data store; (3) transforming the data; (4) extracting one or more feature and metric from the transformed data; (5) feeding the feature and metric into a machine learning model to identify at least one contributing metric; and (6) associating the contributing metric with at least one incident.

Systems and methods for machine-learning augmented application monitoring

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New York, NY, United States of America

Sergul Aydore