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Differentially private contrastive learning no longer needs to sacrifice so much accuracy, thanks to a new method that cleverly bounds gradient dependencies.
Forget black-box clustering: WISE delivers state-of-the-art performance on mixed-type data while providing transparent, feature-level explanations grounded in the same primitives that drive clustering.
Sampling the wrong data in differentially private queries can inflate error by 10x, but a new method slashes that overhead by sampling aggregation units instead of users.
