empirical risk minimization

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization ICML 2020 Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization ICML 2020 Towards Problem-dependent Optimal Learning Rates NeurIPS 2020 Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches AISTATS 2019 Acceleration of SVRG and Katyusha X by Inexact Preconditioning ICML 2019 A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression AAAI 2019 A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity ALT 2019 Average Individual Fairness: Algorithms, Generalization and Experiments NeurIPS 2019 Better generalization with less data using robust gradient descent ICML 2019 Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy ICML 2019 Classification from Positive, Unlabeled and Biased Negative Data ICML 2019 Computing Full Conformal Prediction Set with Approximate Homotopy NeurIPS 2019 Differentially Private Empirical Risk Minimization with Non-convex Loss Functions ICML 2019 Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View AAAI 2019 Distributionally Robust Submodular Maximization AISTATS 2019 Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods AISTATS 2019 Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data AISTATS 2019 Latent Semantics Encoding for Label Distribution Learning IJCAI 2019 Learning Weighted Top-k Support Vector Machine ACML 2019 Learning with Non-Convex Truncated Losses by SGD UAI 2019 Multi-Observation Regression AISTATS 2019 Nonconvex Variance Reduced Optimization with Arbitrary Sampling ICML 2019 Online Learning from Data Streams with Varying Feature Spaces AAAI 2019 On Medians of (Randomized) Pairwise Means ICML 2019 On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data ICLR 2019