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Found 11 papers, 1 papers with code
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
Markov Games (MG) is an important model for Multi-Agent Reinforcement Learning (MARL).
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
In this work, we provide a different perspective based on online learning that underscores the importance of Adam's algorithmic components.
Refined Regret for Adversarial MDPs with Linear Function Approximation
This analysis allows the loss estimators to be arbitrarily negative and might be of independent interest.
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
\texttt{Banker-OMD} leads to the first delayed scale-free adversarial MAB algorithm achieving $\widetilde{\mathcal O}(\sqrt{K}L(\sqrt T+\sqrt D))$ regret and the first delayed adversarial linear bandit algorithm achieving $\widetilde{\mathcal O}(\text{poly}(n)(\sqrt{T} + \sqrt{D}))$ regret.
Skeleton-based Action Recognition via Adaptive Cross-Form Learning
Specifically, each GCN model in ACFL not only learns action representation from the single-form skeletons, but also adaptively mimics useful representations derived from other forms of skeletons.
Variance-Aware Sparse Linear Bandits
On the other hand, in the benign setting where there is no noise and the action set is the unit sphere, one can use divide-and-conquer to achieve $\widetilde{\mathcal O}(1)$ regret, which is (nearly) independent of $d$ and $T$.
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
More importantly, we then find two significant applications: First, the analysis of FTPL turns out to be readily generalizable to delayed bandit feedback with order-optimal regret, while OMD methods exhibit extra difficulties (Jin et al., 2022).
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Specifically, we design an algorithm \texttt{HTINF}, when the heavy-tail parameters $\alpha$ and $\sigma$ are known to the agent, \texttt{HTINF} simultaneously achieves the optimal regret for both stochastic and adversarial environments, without knowing the actual environment type a-priori.
Scale-Free Adversarial Multi-Armed Bandit with Arbitrary Feedback Delays
We consider the Scale-Free Adversarial Multi-Armed Bandit (MAB) problem with unrestricted feedback delays.
From Knowledge Map to Mind Map: Artificial Imagination
Imagination is one of the most important factors which makes an artistic painting unique and impressive.
Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region
Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time.
