Search Results - "Zhang, Shangtong"

Growth and Survival of Fusarium solani-F. oxysporum Complex on Stressed Multipurpose Contact Lens Care Solution Films on Plastic Surfaces In Situ and In Vitro by Zhang, Shangtong, Ahearn, Donald G, Noble-Wang, Judith A, Stulting, R Doyle, Schwam, Brian L, Simmons, Robert B, Pierce, George E, Crow, Sidney A

“…PURPOSE:To analyze factors implicating the association of ReNu with MoistureLoc (ReNu ML) multipurpose contact lens solution (MPS) with the increased incidence…”
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IMGA: Efficient In-Memory Graph Convolution Network Aggregation with Data Flow Optimizations by Wei, Yuntao, Wang, Xueyan, Zhang, Shangtong, Yang, Jianlei, Jia, Xiaotao, Wang, Zhaohao, Qu, Gang, Zhao, Weisheng

“…Aggregating features from neighbor vertices is a fundamental operation in Graph Convolution Network (GCN). However, the sparsity in graph data creates poor…”
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Breaking the Deadly Triad in Reinforcement Learning by Zhang, Shangtong

“…Reinforcement Learning (RL) is a promising framework for solving sequential decision making problems emerging from agent-environment interactions via trial and…”
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Toxic Effects of Ag(I) and Hg(II) on Candida albicans and C. maltosa: a Flow Cytometric Evaluation by Zhang, S, Crow, Jr, S A

“…Classifications Services AEM Citing Articles Google Scholar PubMed Related Content Social Bookmarking CiteULike Delicious Digg Facebook Google+ Mendeley Reddit…”
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Almost Sure Convergence of Average Reward Temporal Difference Learning by Blaser, Ethan, Zhang, Shangtong

“…Tabular average reward Temporal Difference (TD) learning is perhaps the simplest and the most fundamental policy evaluation algorithm in average reward…”
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Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features by Wang, Jiuqi, Zhang, Shangtong

“…Temporal difference (TD) learning with linear function approximation, abbreviated as linear TD, is a classic and powerful prediction algorithm in reinforcement…”
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Direct Gradient Temporal Difference Learning by Qian, Xiaochi, Zhang, Shangtong

“…Off-policy learning enables a reinforcement learning (RL) agent to reason counterfactually about policies that are not executed and is one of the most…”
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Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design by Liu, Shuze, Zhang, Shangtong

“…Most reinforcement learning practitioners evaluate their policies with online Monte Carlo estimators for either hyperparameter tuning or testing different…”
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mlpack 3: a fast, flexible machine learning library by R. Curtin, Ryan, Edel, Marcus, Lozhnikov, Mikhail, Mentekidis, Yannis, Ghaisas, Sumedh, Zhang, Shangtong

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Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning by Chen, Claire, Liu, Shuze, Zhang, Shangtong

“…In reinforcement learning, classic on-policy evaluation methods often suffer from high variance and require massive online data to attain the desired accuracy…”
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Doubly Optimal Policy Evaluation for Reinforcement Learning by Liu, Shuze, Chen, Claire, Zhang, Shangtong

“…Policy evaluation estimates the performance of a policy by (1) collecting data from the environment and (2) processing raw data into a meaningful estimate. Due…”
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Truncated Emphatic Temporal Difference Methods for Prediction and Control by Zhang, Shangtong, Whiteson, Shimon

“…Emphatic Temporal Difference (TD) methods are a class of off-policy Reinforcement Learning (RL) methods involving the use of followon traces. Despite the…”
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Efficient Multi-Policy Evaluation for Reinforcement Learning by Liu, Shuze, Chen, Yuxin, Zhang, Shangtong

“…To unbiasedly evaluate multiple target policies, the dominant approach among RL practitioners is to run and evaluate each target policy separately. However,…”
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The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise by Liu, Shuze, Chen, Shuhang, Zhang, Shangtong

“…Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e.g., stochastic gradient…”
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CRISP: Triangle Counting Acceleration via Content Addressable Memory-Integrated 3D-Stacked Memory by Zhang, Shangtong, Wang, Xueyan, Zhao, Weisheng, Jin, Yier

“…Triangle Counting is a fundamental problem in graph analysis, which usually needs to traverse the graph and perform set-intersections of neighbor sets…”
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Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning by Wang, Jiuqi, Blaser, Ethan, Daneshmand, Hadi, Zhang, Shangtong

“…In-context learning refers to the learning ability of a model during inference time without adapting its parameters. The input (i.e., prompt) to the model…”
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In vitro interactions of Fusarium and Acanthamoeba with drying residues of multipurpose contact lens solutions by Ahearn, Donald G, Zhang, Shangtong, Stulting, R Doyle, Simmons, Robert B, Ward, Michael A, Pierce, George E, Crow, Jr, Sidney A

“…To examine in vitro effects of evaporation and drying of multipurpose contact lens solutions on survival of Fusarium and Acanthamoeba. Conidia of…”
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DAC: The Double Actor-Critic Architecture for Learning Options by Zhang, Shangtong, Whiteson, Shimon

“…We reformulate the option framework as two parallel augmented MDPs. Under this novel formulation, all policy optimization algorithms can be used off the shelf…”
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On the Convergence of SARSA with Linear Function Approximation by Zhang, Shangtong, Tachet, Remi, Laroche, Romain

“…SARSA, a classical on-policy control algorithm for reinforcement learning, is known to chatter when combined with linear function approximation: SARSA does not…”
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Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch by Zhang, Shangtong, Tachet, Remi, Laroche, Romain

“…In this paper, we establish the global optimality and convergence rate of an off-policy actor critic algorithm in the tabular setting without using density…”
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