convex loss

Near-optimal Swap Regret Minimization for Convex Losses COLT 2026 Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice ICML 2023 Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration NeurIPS 2023 Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets COLT 2023 Differentially Private Generalized Linear Models Revisited NeurIPS 2022 Distributionally Robust Optimization via Ball Oracle Acceleration NeurIPS 2022 Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension ICML 2022 Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions JMLR 2021 Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions NeurIPS 2021 Projection-free Online Learning over Strongly Convex Sets AAAI 2021 Regularized ERM on random subspaces AISTATS 2021 Remember What You Want to Forget: Algorithms for Machine Unlearning NeurIPS 2021 Choosing the Sample with Lowest Loss makes SGD Robust AISTATS 2020 Large-Scale Methods for Distributionally Robust Optimization NeurIPS 2020 Robust high dimensional learning for Lipschitz and convex losses JMLR 2020 Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions AISTATS 2020 Approximation Hardness for A Class of Sparse Optimization Problems JMLR 2019 Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games AAAI 2019 Safe Grid Search with Optimal Complexity ICML 2019 Convex Elicitation of Continuous Properties NeurIPS 2018 Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global ICML 2018 Memoryless Sequences for Differentiable Losses COLT 2017 Multi-Positive and Unlabeled Learning IJCAI 2017 Differential Privacy without Sensitivity NeurIPS 2016 Generalization Properties and Implicit Regularization for Multiple Passes SGM ICML 2016