global convergence

PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping AAAI 2024 Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes AAAI 2024 Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates NeurIPS 2024 Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models NeurIPS 2024 Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles ICML 2024 Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees AISTATS 2023 Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence AAAI 2023 Coordinate Descent Methods for Fractional Minimization ICML 2023 Exponentially Convergent Algorithms for Supervised Matrix Factorization NeurIPS 2023 Federated Learning with Bilateral Curation for Partially Class-Disjoint Data NeurIPS 2023 Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization NeurIPS 2023 Global Convergence of Over-parameterized Deep Equilibrium Models AISTATS 2023 Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization JMLR 2023 Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator AAAI 2023 Global Optimality in Bivariate Gradient-based DAG Learning NeurIPS 2023 Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks ICML 2023 Leveraging the two-timescale regime to demonstrate convergence of neural networks NeurIPS 2023 Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence COLT 2023 On the Convergence of Encoder-only Shallow Transformers NeurIPS 2023 On the Convergence of Gradient Flow on Multi-layer Linear Models ICML 2023 On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms NeurIPS 2023 On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization ICML 2023 On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures ICML 2023 Ordering-based Conditions for Global Convergence of Policy Gradient Methods NeurIPS 2023 Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron COLT 2023