Search Results for author: Robert Nishihara

Found 14 papers, 8 papers with code

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

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.


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.


Real-Time Machine Learning: The Missing Pieces

2 code implementations11 Mar 2017 Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael. I. Jordan, Ion Stoica

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making.

BIG-bench Machine Learning Decision Making

Discovering Causal Signals in Images

1 code implementation CVPR 2017 David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou

Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.

Causal Discovery

SparkNet: Training Deep Networks in Spark

1 code implementation19 Nov 2015 Philipp Moritz, Robert Nishihara, Ion Stoica, Michael. I. Jordan

We introduce SparkNet, a framework for training deep networks in Spark.

No Regret Bound for Extreme Bandits

no code implementations12 Aug 2015 Robert Nishihara, David Lopez-Paz, Léon Bottou

This work is naturally framed in the extreme bandit setting, which deals with sequentially choosing which distribution from a collection to sample in order to minimize (maximize) the single best cost (reward).

Hyperparameter Optimization

A Linearly-Convergent Stochastic L-BFGS Algorithm

1 code implementation9 Aug 2015 Philipp Moritz, Robert Nishihara, Michael. I. Jordan

We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions.

A General Analysis of the Convergence of ADMM

no code implementations6 Feb 2015 Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew Packard, Michael. I. Jordan

We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex.

Optimization and Control Numerical Analysis

Detecting Parameter Symmetries in Probabilistic Models

no code implementations19 Dec 2013 Robert Nishihara, Thomas Minka, Daniel Tarlow

Probabilistic models often have parameters that can be translated, scaled, permuted, or otherwise transformed without changing the model.

Probabilistic Programming

Parallel MCMC with Generalized Elliptical Slice Sampling

no code implementations28 Oct 2012 Robert Nishihara, Iain Murray, Ryan P. Adams

Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them.

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