Optimization

Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification JMLR 2023 Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent NeurIPS 2023 Predefined-Time Convergent Motion Control for Heterogeneous Continuum Robots RSS 2023 Predictive Flows for Faster Ford-Fulkerson ICML 2023 Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints AAAI 2023 Predictor-corrector algorithms for stochastic optimization under gradual distribution shift ICLR 2023 Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation AAAI 2023 Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers NeurIPS 2023 Pre-training for Speech Translation: CTC Meets Optimal Transport ICML 2023 Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems ICML 2023 Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation NeurIPS 2023 Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models AAAI 2023 PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning NeurIPS 2023 Prioritizing Samples in Reinforcement Learning with Reducible Loss NeurIPS 2023 PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023 PRISE: Demystifying Deep Lucas-Kanade With Strongly Star-Convex Constraints for Multimodel Image Alignment CVPR 2023 Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023 Privacy-Preserved Evolutionary Graph Modeling via Gromov-Wasserstein Autoregression AAAI 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance NeurIPS 2023 Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses ICLR 2023 Private Non-Convex Federated Learning Without a Trusted Server AISTATS 2023 Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks NeurIPS 2023 Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses ALT 2023 Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse ICLR 2023 Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models ICML 2023