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dynamic regret

94 papers

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Co-occurring keywords

online learning (1777) online convex optimization (165) regret bound (1926) non-stationary environment (61) online algorithm (445) convex optimization (1321) adaptive regret (19) multi-armed bandit (1098) online gradient descent (39) policy optimization (657)

Papers

A Primal-Dual Online Algorithm for Online Matching Problem in Dynamic Environments AAAI 2021
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding JMLR 2021
Bandit Convex Optimization in Non-stationary Environments JMLR 2021
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions L4DC 2021
Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs ICML 2021
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging ICML 2021
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems NIPS 2021
Optimal Dynamic Regret in Exp-Concave Online Learning COLT 2021
Non-stationary Reinforcement Learning without Prior Knowledge: an Optimal Black-box Approach COLT 2021
On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems AAAI 2021
Stochastic Bandits with Graph Feedback in Non-Stationary Environments AAAI 2021
Revisiting Smoothed Online Learning NIPS 2021
Projection-free Online Learning in Dynamic Environments AAAI 2021
Dynamic Regret of Convex and Smooth Functions NIPS 2020
Online Learning in Changing Environments IJCAI 2020
Adaptive Online Estimation of Piecewise Polynomial Trends NIPS 2020
Adversarial Blocking Bandits NIPS 2020
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes NIPS 2020
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism ICML 2020
A Simple Approach for Non-stationary Linear Bandits AISTATS 2020
Bandit Convex Optimization in Non-stationary Environments AISTATS 2020
Online Learning with Continuous Variations: Dynamic Regret and Reductions AISTATS 2020
Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent ICML 2020
Optimizing for the Future in Non-Stationary MDPs ICML 2020
Minimizing Dynamic Regret and Adaptive Regret Simultaneously AISTATS 2020