599 citations · 1.6k across the 13 of their papers we have counts for
14 papers
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
Siyuan Zhuang, Zhuohan Li, Danyang Zhuo +5
Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including…
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Thomas Anthony, Robert Nishihara, Philipp Moritz +2
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising…
Tune: A Research Platform for Distributed Model Selection and Training
Richard Liaw, Eric Liang, Robert Nishihara +3
Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonabl…
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz +6
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
Philipp Moritz, Robert Nishihara, Stephanie Wang +8
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requir…
Real-Time Machine Learning: The Missing Pieces
Robert Nishihara, Philipp Moritz, Stephanie Wang +7
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dyn…
Discovering Causal Signals in Images
David Lopez-Paz, Robert Nishihara, Soumith Chintala +2
This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goa…
SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica +1
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a clus…
No Regret Bound for Extreme Bandits
Robert Nishihara, David Lopez-Paz, Léon Bottou
Algorithms for hyperparameter optimization abound, all of which work well under different and often unverifiable assumptions. Motivated by the general challenge of sequentially cho…
A Linearly-Convergent Stochastic L-BFGS Algorithm
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. Our algorithm draws heavily from a recent stochastic vari…
A General Analysis of the Convergence of ADMM
Robert Nishihara, Laurent Lessard, Benjamin Recht +2
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. Our proof is based on…
On the Convergence Rate of Decomposable Submodular Function Minimization
Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
Submodular functions describe a variety of discrete problems in machine learning, signal processing, and computer vision. However, minimizing submodular functions poses a number of…
Detecting Parameter Symmetries in Probabilistic Models
Robert Nishihara, Thomas Minka, Daniel Tarlow
Probabilistic models often have parameters that can be translated, scaled, permuted, or otherwise transformed without changing the model. These symmetries can lead to strong correl…
Parallel MCMC with Generalized Elliptical Slice Sampling
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.…