Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss

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A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm’s privacy loss remain open — even in the seemingly simple setting of smooth convex losses over …

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FLAIR: Federated Learning Annotated Image Repository

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Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing …

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Two-Layer Bandit Optimization for Recommendations

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Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is …

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Ontology: Finding meaning in data (Palantir RFx Blog Series, #1)

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A functional data ecosystem must incorporate notions of Ontology in order to be scalable and sustainable. Editor’s note: This is the first post in the Palantir RFx Blog Series, which breaks down some of the key pillars of a data ecosystem using language commonly found in formal solicitations such as RFIs and RFPs. Each post …

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Evaluating Software (Palantir RFx Blog Series, #0)

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This series tackles the rarely simple and often messy solicitation process. We explore how organizations can better evaluate digital transformation software. Welcome to the RFx Blog Series, which explores the question: how should commercial organizations evaluate digital transformation software? In this series, we use language commonly found in formal solicitations, including specific questions and functional …

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Trustworthy AI helps provide equitable preventative care for diabetics

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There are over 30 million people in America who have diabetes, and people with diabetes need to remain vigilant about their health. They need the extra attention and resources provided by their healthcare systems because, unfortunately, around 38% to 40% of people with diabetes end up visiting the ER due to complications. Healthcare organizations – …

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Create high-quality data for ML models with Amazon SageMaker Ground Truth

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Machine learning (ML) has improved business across industries in recent years—from the recommendation system on your Prime Video account, to document summarization and efficient search with Alexa’s voice assistance. However, the question remains of how to incorporate this technology into your business. Unlike traditional rule-based methods, ML automatically infers patterns from data so as to …

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Automate your time series forecasting in Snowflake using Amazon Forecast

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This post is a joint collaboration with Andries Engelbrecht and James Sun of Snowflake, Inc. The cloud computing revolution has enabled businesses to capture and retain corporate and organizational data without capacity planning or data retention constraints. Now, with diverse and vast reserves of longitudinal data, companies are increasingly able to find novel and impactful …

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Achieve four times higher ML inference throughput at three times lower cost per inference with Amazon EC2 G5 instances for NLP and CV PyTorch models

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Amazon Elastic Compute Cloud (Amazon EC2) G5 instances are the first and only instances in the cloud to feature NVIDIA A10G Tensor Core GPUs, which you can use for a wide range of graphics-intensive and machine learning (ML) use cases. With G5 instances, ML customers get high performance and a cost-efficient infrastructure to train and …

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