empirical risk

Learning discrete distributions with infinite support NeurIPS 2020 Positive Unlabeled Learning with Class-prior Approximation IJCAI 2020 Global Guarantees for Blind Demodulation with Generative Priors NeurIPS 2019 Minimizers of the Empirical Risk and Risk Monotonicity NeurIPS 2019 Surfing: Iterative Optimization Over Incrementally Trained Deep Networks NeurIPS 2019 The Landscape of Non-convex Empirical Risk with Degenerate Population Risk NeurIPS 2019 Deep Learning with Logged Bandit Feedback ICLR 2018 Empirical Risk Landscape Analysis for Understanding Deep Neural Networks ICLR 2018 On the Local Minima of the Empirical Risk NeurIPS 2018 Phase Retrieval Under a Generative Prior NeurIPS 2018 Understanding Generalization and Optimization Performance of Deep CNNs ICML 2018 Uniform Convergence of Gradients for Non-Convex Learning and Optimization NeurIPS 2018 A Learning Theory of Ranking Aggregation AISTATS 2017 Handling Noise in Boolean Matrix Factorization IJCAI 2017 Positive-Unlabeled Learning with Non-Negative Risk Estimator NeurIPS 2017 Loss factorization, weakly supervised learning and label noise robustness ICML 2016 An LP for Sequential Learning Under Budgets AISTATS 2014 Active Comparison of Prediction Models NeurIPS 2012 Learning Probability Measures with respect to Optimal Transport Metrics NeurIPS 2012 Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL NeurIPS 2012 On Ranking and Generalization Bounds JMLR 2012 The Fast Convergence of Boosting NeurIPS 2011 Rademacher Complexities and Bounding the Excess Risk in Active Learning JMLR 2010 On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality JMLR 2009