convergence guarantee

PFedAtt: Attention-based Personalized Federated Learning on Heterogeneous Clients ACML 2021 Provable Hierarchical Imitation Learning via EM AISTATS 2021 Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence AISTATS 2021 Towards Flexible Device Participation in Federated Learning AISTATS 2021 Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond NeurIPS 2021 Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent IJCAI 2020 Asynchronous Coagent Networks ICML 2020 Extreme Tensoring for Low-Memory Preconditioning ICLR 2020 Geometric Insights into the Convergence of Nonlinear TD Learning ICLR 2020 Gradient descent algorithms for Bures-Wasserstein barycenters COLT 2020 Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes NeurIPS 2020 On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems NeurIPS 2020 Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality NeurIPS 2020 Provable Smoothness Guarantees for Black-Box Variational Inference ICML 2020 Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation ICML 2020 Representations for Stable Off-Policy Reinforcement Learning ICML 2020 SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum ICLR 2020 Stochastically Dominant Distributional Reinforcement Learning ICML 2020 Stochastic Approximate Gradient Descent via the Langevin Algorithm AAAI 2020 Universal Average-Case Optimality of Polyak Momentum ICML 2020 A Comparative Analysis of Expected and Distributional Reinforcement Learning AAAI 2019 A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks ICLR 2019 A Convergence Theory for Deep Learning via Over-Parameterization ICML 2019 Adaptive Gradient Methods with Dynamic Bound of Learning Rate ICLR 2019 A Kernel Loss for Solving the Bellman Equation NeurIPS 2019