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We present our recent research progress on joint-optimization for ML algorithm and SVD solver. Our major discovery is that the "stopping criteria" of a SVD algorithm can be directly optimized for downstream ML applications. We will present a few examples, in which when we change the stopping criteria of the "inner SVD algorithm", we see significant performance gain (in running time) in the "outer" ML algorithm.