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Showing 1 to 4 of 4 for “"Minimax Framework"”.
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A Unified Robust Minimax Framework for Regularized Learning Problems
… model. In this work, we propose a robust minimax formulation to interpret the relationship between data and regularization terms for a large class of loss functions. We show that various regularization terms are essentially corresponding to different distortions to the original data …
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On Semi-supervised Estimation of Distributions
… samples missing one fixed variable. We adopt the minimax framework with [notation] loss functions, and we show that the composition of uni-variate minimax estimators achieves minimax risk with the optimal first-order constant for 𝑝 ≥ 2, in the regime 𝑚 = 𝑜(𝑛).
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A Minimax Approach for Learning Gaussian Mixtures
… Training for Gaussian Mixture Models (GATGMM), a minimax GAN framework for learning GMMs. Motivated by optimal transport theory, we design the zero-sum game in GAT-GMM using a random linear generator and a softmax-based quadratic discriminator architecture, which leads to a non-convex concave …
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Consistency of nonparametric Bayesian methods for two statistical inverse problems arising from partial differential equations
… the true drift function, at the frequentist minimax rate (up to logarithmic factors) over periodic Besov smoothness classes. These conditions are verified for some natural nonparametric priors, some of which are shown to adapt to an unknown smoothness parameter. The results are given in the …