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Found 9 papers, 0 papers with code
Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment
In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability.
Stability Selection for Structured Variable Selection
Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives.
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
Distributed machine learning has typically been approached from a data parallel perspective, where big data are partitioned to multiple workers and an algorithm is executed concurrently over different data subsets under various synchronization schemes to ensure speed-up and/or correctness.
Screening Rules for Overlapping Group Lasso
However, screening for overlapping group lasso remains an open challenge because the overlaps between groups make it infeasible to test each group independently.
Primitives for Dynamic Big Model Parallelism
When training large machine learning models with many variables or parameters, a single machine is often inadequate since the model may be too large to fit in memory, while training can take a long time even with stochastic updates.
Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)?
Structure-Aware Dynamic Scheduler for Parallel Machine Learning
Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates.
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees.
Adaptive Multi-Task Lasso: with Application to eQTL Detection
To understand the relationship between genomic variations among population and complex diseases, it is essential to detect eQTLs which are associated with phenotypic effects.
