follow the regularized leader

A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs JMLR 2025 Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching COLT 2025 Divergence-Regularized Discounted Aggregation: Equilibrium Finding in Multiplayer Partially Observable Stochastic Games ICLR 2025 NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks ICML 2024 Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg ICML 2024 Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise ICML 2024 A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs COLT 2023 Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm COLT 2023 Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness NeurIPS 2023 Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits ICML 2023 Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds COLT 2022 Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs NeurIPS 2022 Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits COLT 2022 Scale-Free Adversarial Multi Armed Bandits ALT 2022 Follow-the-Regularized-Leader Routes to Chaos in Routing Games ICML 2021 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization ICML 2021 Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions ICML 2021 Refined approachability algorithms and application to regret minimization with global costs JMLR 2021 Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information COLT 2021 An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays AISTATS 2020 No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix NeurIPS 2020 Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses NeurIPS 2020 Exponential Weights on the Hypercube in Polynomial Time AISTATS 2019 On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits UAI 2019 Horizon-Independent Minimax Linear Regression NeurIPS 2018