Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data

The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To prevent leakage of sensitive data, we consider using differentially-private (DP), synthetic training data instead of real training data to train an ML model. A key desirable property of synthetic data is its ability to preserve the low-order marginals of the original distribution. Our main contribution comprises novel upper and lower bounds on the excess empirical risk of linear models trained on such synthetic data, for continuous and Lipschitz loss functions. We perform extensive experimentation alongside our theoretical results.

Mystique: Accurate and Scalable Production AI Benchmarks Generation

Building and maintaining large AI fleets to efficiently support the fast-growing DL workloads is an active research topic for modern cloud infrastructure providers. Generating accurate benchmarks plays an essential role in the design and evaluation of rapidly evoloving software and hardware solutions in this area. Two fundamental challenges to make this process scalable are (i) workload representativeness and (ii) the ability to quickly incorporate changes to the fleet into the benchmarks. To overcome these issues, we propose Mystique, an accurate and scalable framework for production AI benchmark generation. It leverages the PyTorch execution graph (EG), a new feature that captures the runtime information of AI models at the granularity of operators, in a graph format, together with their metadata. By sourcing EG traces from the fleet, we can build AI benchmarks that are portable and representative. Mystique is scalable, with its lightweight data collection, in terms of runtime overhead and user instrumentation efforts. It is also adaptive, as the expressiveness and composability of EG format allows flexible user control over benchmark creation. We evaluate our methodology on several production AI workloads, and show that benchmarks generated with Mystique closely resemble original AI models, both in execution time and system-level metrics. We also showcase the portability of the generated benchmarks across platforms, and demonstrate several use cases enabled by the fine-grained composability of the execution graph.