conftrace_

optimistic algorithm

35 papers

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Co-occurring keywords

regret bound (1926) online learning (1777) multi-armed bandit (1098) regret minimization (315) markov decision process (790) reinforcement learning (4352) nash equilibrium (398) delayed feedback (54) stochastic optimization (1060) linear bandit (124)

Papers

On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning ICML 2025
Learning-Based Algorithms for Graph Searching Problems AISTATS 2024
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints NIPS 2024
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits NIPS 2024
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits NIPS 2024
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning NIPS 2024
Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence beyond the Minty Property AAAI 2024
Multi-armed bandits with guaranteed revenue per arm AISTATS 2024
Optimistic PAC Reinforcement Learning: the Instance-Dependent View ALT 2023
Optimism and Delays in Episodic Reinforcement Learning AISTATS 2023
Delayed Feedback in Generalised Linear Bandits Revisited AISTATS 2023
Dynamical Linear Bandits ICML 2023
A Unified Algorithm for Stochastic Path Problems ALT 2023
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning ICML 2023
Learning in Markov games: Can we exploit a general-sum opponent? UAI 2022
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning NIPS 2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection NIPS 2021
Online Learning with Optimism and Delay ICML 2021
Last-iterate Convergence in Extensive-Form Games NIPS 2021
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond NIPS 2021
Local Differential Privacy for Regret Minimization in Reinforcement Learning NIPS 2021
Improved Optimistic Algorithms for Logistic Bandits ICML 2020
Randomized Exploration for Non-Stationary Stochastic Linear Bandits UAI 2020
A simpler approach to accelerated optimization: iterative averaging meets optimism ICML 2020
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret NIPS 2020