Richard Liaw

According to our database, Richard Liaw authored at least 18 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale.

Richard Liaw, Paige Bailey, Ying Li, Maria Dimakopoulou, Yves Raimond

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

ESCHER: expressive scheduling with ephemeral resources.

Romil Bhardwaj, Alexey Tumanov, Stephanie Wang, Richard Liaw, Philipp Moritz, Robert Nishihara, Ion Stoica

Proceedings of the 13th Symposium on Cloud Computing, SoCC 2022, 2022

2021

RubberBand: cloud-based hyperparameter tuning.

Ujval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj, Kirthevasan Kandasamy, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

Proceedings of the EuroSys '21: Sixteenth European Conference on Computer Systems, 2021

Elastic Hyperparameter Tuning on the Cloud.

Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez

Proceedings of the SoCC '21: ACM Symposium on Cloud Computing, 2021

2020

IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks.

Michael Luo, Jiahao Yao, Richard Liaw, Eric Liang, Ion Stoica

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards.

Sanjay Krishnan, Animesh Garg, Richard Liaw, Brijen Thananjeyan, Lauren Miller, Florian T. Pokorny, Ken Goldberg

Int. J. Robotics Res., 2019

HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline.

Richard Liaw, Romil Bhardwaj, Lisa Dunlap, Yitian Zou, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

Proceedings of the ACM Symposium on Cloud Computing, SoCC 2019, 2019

2018

Tune: A Research Platform for Distributed Model Selection and Training.

Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, Ion Stoica

CoRR, 2018

Ray: A Distributed Framework for Emerging AI Applications.

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

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

RLlib: Abstractions for Distributed Reinforcement Learning.

Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica

Proceedings of the 35th International Conference on Machine Learning, 2018

Benchmarks for reinforcement learning in mixed-autonomy traffic.

Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu, Fangyu Wu, Richard Liaw, Eric Liang, Alexandre M. Bayen

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2017

Ray RLLib: A Composable and Scalable Reinforcement Learning Library.

Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Joseph Gonzalez, Ken Goldberg, Ion Stoica

CoRR, 2017

Ray: A Distributed Framework for Emerging AI Applications.

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, William Paul, Michael I. Jordan, Ion Stoica

CoRR, 2017

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning.

Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg

CoRR, 2017

Iterative Noise Injection for Scalable Imitation Learning.

Michael Laskey, Jonathan Lee, Wesley Yu-Shu Hsieh, Richard Liaw, Jeffrey Mahler, Roy Fox, Ken Goldberg

CoRR, 2017

Real-Time Machine Learning: The Missing Pieces.

Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica

Proceedings of the 16th Workshop on Hot Topics in Operating Systems, 2017

2016

HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards.

Sanjay Krishnan, Animesh Garg, Richard Liaw, Lauren Miller, Florian T. Pokorny, Ken Goldberg

CoRR, 2016

SWIRL: A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards.

Sanjay Krishnan, Animesh Garg, Richard Liaw, Brijen Thananjeyan, Lauren Miller, Florian T. Pokorny, Ken Goldberg

Proceedings of the Algorithmic Foundations of Robotics XII, 2016