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Provably private tabular classification is now achievable with a single forward pass, outperforming baselines and drastically cutting fitting time.
Unlock probabilistic PDE modeling from existing deterministic backbones with a simple retrofitting technique that slashes error by up to 54% and works across diverse architectures.
General-purpose Causal Foundation Models can now match the performance of specialized causal models by incorporating partial causal graph information via attention bias, unlocking a more unified approach to causal inference.
