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Submodular Optimization: From Discrete to Continuous and Back

34-101

Abstract Many procedures in statistics and artificial intelligence require solving non-convex problems. Historically, the focus has been to convexify the non-convex objectives. In recent years, however, there has been significant progress to optimize non-convex functions directly. This direct approach has led to provably good guarantees for specific problem instances such as latent variable models, non-negative…


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