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Publications (5)
In this paper, we propose a robust method for the estimation of regression function. By symmetric addition, we change platykurtic errors into leptokurtic errors; and then estimate the nonparametric function by the local polynomial least absolute deviation regression. Different from the local polynomial least squares estimator, the new estimator is...
In this paper, we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking, metric learning, area under...
In this paper we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking, metric learning, area under R...
In this paper, we propose a robust method for the estimation of regression function. By symmetric addition, we change platykurtic errors into lep-tokurtic errors; and then estimate the nonparametric function by the local polynomial least absolute deviation regression. Different from the local polynomial least squares estimator, the new estimator is...
