population risk

Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection ICML 2025 Generalization and Robustness of the Tilted Empirical Risk ICML 2025 Differentially Private Worst-group Risk Minimization ICML 2024 Noisy Interpolation Learning with Shallow Univariate ReLU Networks ICLR 2024 Symmetries in Overparametrized Neural Networks: A Mean Field View NeurIPS 2024 Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance JMLR 2024 Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods ICML 2023 Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems COLT 2023 Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance NeurIPS 2023 Benign Underfitting of Stochastic Gradient Descent NeurIPS 2022 Differentially Private Generalized Linear Models Revisited NeurIPS 2022 Private Convex Optimization via Exponential Mechanism COLT 2022 Stability and Generalization for Markov Chain Stochastic Gradient Methods NeurIPS 2022 Differentially Private Pairwise Learning Revisited IJCAI 2021 Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime JMLR 2021 Private Stochastic Non-convex Optimization with Improved Utility Rates IJCAI 2021 What Makes Multi-Modal Learning Better than Single (Provably) NeurIPS 2021 Agnostic Learning of a Single Neuron with Gradient Descent NeurIPS 2020 Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint ICLR 2020 Information-Theoretic Understanding of Population Risk Improvement with Model Compression AAAI 2020 Scalable Approximations for Generalized Linear Problems JMLR 2019 The Landscape of Non-convex Empirical Risk with Degenerate Population Risk NeurIPS 2019 Learning One-hidden-layer Neural Networks with Landscape Design ICLR 2018 On the Local Minima of the Empirical Risk NeurIPS 2018 A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics COLT 2017