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This paper presents a randomized algorithm that solves k-coloring on n-vertex graphs in time (2-ε_k)^n for every fixed k, resolving a long-standing open problem in exponential-time algorithms. It builds on hypergraph containers and list-coloring reductions.
RGLD combines global random-feature density estimation with local neighbor analysis for efficient unsupervised tabular anomaly detection, achieving top AUROC performance on 47 datasets while being 50x-580x faster than deep detectors.
This paper introduces a new differential privacy sketching mechanism based on fast transforms that achieves state-of-the-art privacy guarantees and improved runtime, and applies it to DP linear regression to obtain the first fast method for DP ordinary least squares.