Search Results for author:
Found 14 papers, 8 papers with code
Parallel MCMC with Generalized Elliptical Slice Sampling
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.
Detecting Parameter Symmetries in Probabilistic Models
Probabilistic models often have parameters that can be translated, scaled, permuted, or otherwise transformed without changing the model.
On the Convergence Rate of Decomposable Submodular Function Minimization
Submodular functions describe a variety of discrete problems in machine learning, signal processing, and computer vision.
A General Analysis of the Convergence of ADMM
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
A Linearly-Convergent Stochastic L-BFGS Algorithm
We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions.
No Regret Bound for Extreme Bandits
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).
SparkNet: Training Deep Networks in Spark
We introduce SparkNet, a framework for training deep networks in Spark.
Discovering Causal Signals in Images
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.
Real-Time Machine Learning: The Missing Pieces
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.
Ray: A Distributed Framework for Emerging AI Applications
To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.
RLlib: Abstractions for Distributed Reinforcement Learning
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.
Tune: A Research Platform for Distributed Model Selection and Training
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.
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online.
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
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.
