145 citations · 149 across the 3 of their papers we have counts for
8 papers
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Eric Liang, Zhanghao Wu, Michael Luo +3
Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the…
Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica +3
Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range…
NeuroCard: One Cardinality Estimator for All Tables
Zongheng Yang, Amog Kamsetty, Sifei Luan +4
Query optimizers rely on accurate cardinality estimates to produce good execution plans. Despite decades of research, existing cardinality estimators are inaccurate for complex que…
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
Michael Luo, Jiahao Yao, Richard Liaw +2
The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinfor…
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Daniel Ho, Eric Liang, Ion Stoica +2
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. Properly…
Deep Unsupervised Cardinality Estimation
Zongheng Yang, Eric Liang, Amog Kamsetty +7
Cardinality estimation has long been grounded in statistical tools for density estimation. To capture the rich multivariate distributions of relational tables, we propose the use o…