Search Results for author: Eric Liang

Found 11 papers, 9 papers with code

RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

1 code implementation NeurIPS 2021 Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica

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 last few years.

reinforcement-learning

Variable Skipping for Autoregressive Range Density Estimation

1 code implementation ICML 2020 Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen

In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models.

Data Augmentation Density Estimation

NeuroCard: One Cardinality Estimator for All Tables

1 code implementation15 Jun 2020 Zongheng Yang, Amog Kamsetty, Sifei Luan, Eric Liang, Yan Duan, Xi Chen, Ion Stoica

Query optimizers rely on accurate cardinality estimates to produce good execution plans.

Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems

1 code implementation13 Feb 2020 Siyuan Zhuang, Zhuohan Li, Danyang Zhuo, Stephanie Wang, Eric Liang, Robert Nishihara, Philipp Moritz, Ion Stoica

Task-based distributed frameworks (e. g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving.

Distributed Computing reinforcement-learning

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

3 code implementations14 May 2019 Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen

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.

Image Augmentation

Deep Unsupervised Cardinality Estimation

1 code implementation10 May 2019 Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica

To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.

Density Estimation

Neural Packet Classification

no code implementations27 Feb 2019 Eric Liang, Hang Zhu, Xin Jin, Ion Stoica

First, many of the existing solutions are iteratively building a decision tree by splitting nodes in the tree.

Classification General Classification

Tune: A Research Platform for Distributed Model Selection and Training

4 code implementations13 Jul 2018 Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, Ion Stoica

We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation.

Hyperparameter Optimization Model Selection

RLlib: Abstractions for Distributed Reinforcement Learning

3 code implementations ICML 2018 Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.

reinforcement-learning

Ray: A Distributed Framework for Emerging AI Applications

4 code implementations16 Dec 2017 Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael. I. Jordan, Ion Stoica

To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.

reinforcement-learning

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