Zobrazeno 1 - 10
of 185
pro vyhledávání: '"Zhu Ruoqing"'
The Markov assumption in Markov Decision Processes (MDPs) is fundamental in reinforcement learning, influencing both theoretical research and practical applications. Existing methods that rely on the Bellman equation benefit tremendously from this as
Externí odkaz:
http://arxiv.org/abs/2409.14684
Recent advances in dynamic treatment regimes (DTRs) facilitate the search for optimal treatments, which are tailored to individuals' specific needs and able to maximize their expected clinical benefits. However, existing algorithms relying on consist
Externí odkaz:
http://arxiv.org/abs/2310.19300
With the recent advancements of technology in facilitating real-time monitoring and data collection, "just-in-time" interventions can be delivered via mobile devices to achieve both real-time and long-term management and control. Reinforcement learni
Externí odkaz:
http://arxiv.org/abs/2309.13458
We study high-confidence off-policy evaluation in the context of infinite-horizon Markov decision processes, where the objective is to establish a confidence interval (CI) for the target policy value using only offline data pre-collected from unknown
Externí odkaz:
http://arxiv.org/abs/2309.13278
This paper develops a method to detect model structural changes by applying a Corrected Kernel Principal Component Analysis (CKPCA) to construct the so-called central distribution deviation subspaces. This approach can efficiently identify the mean a
Externí odkaz:
http://arxiv.org/abs/2307.07827
Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vital role in developing medical treatment regimes. One challenge in ada
Externí odkaz:
http://arxiv.org/abs/2301.08940
Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a layer-wise approach recursively performs importance sampling to select ne
Externí odkaz:
http://arxiv.org/abs/2206.00583
Survival random forest is a popular machine learning tool for modeling censored survival data. However, there is currently no statistically valid and computationally feasible approach for estimating its confidence band. This paper proposes an unbiase
Externí odkaz:
http://arxiv.org/abs/2204.12038
Infinite-order U-statistics (IOUS) has been used extensively on subbagging ensemble learning algorithms such as random forests to quantify its uncertainty. While normality results of IOUS have been studied extensively, its variance estimation approac
Externí odkaz:
http://arxiv.org/abs/2202.09008
Statistical analysis is increasingly confronted with complex data from metric spaces. Petersen and M\"uller (2019) established a general paradigm of Fr\'echet regression with complex metric space valued responses and Euclidean predictors. However, th
Externí odkaz:
http://arxiv.org/abs/2202.04912