New query-time bound of tilde O(d + epsilon Delta squared + 1/epsilon cubed) for Gaussian kernel mean estimation, improving prior bounds for small epsilon and intermediate diameter via a fast spherical embedding theorem.
SIAM Journal on computing , volume=
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RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.
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New Bounds for Kernel Sums via Fast Spherical Embeddings
New query-time bound of tilde O(d + epsilon Delta squared + 1/epsilon cubed) for Gaussian kernel mean estimation, improving prior bounds for small epsilon and intermediate diameter via a fast spherical embedding theorem.
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RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.