41 citations · 144 across the 7 of their papers we have counts for
8 papers
Hemingway: Modeling Distributed Optimization Algorithms
Xinghao Pan, Shivaram Venkataraman, Zizheng Tai +1
Distributed optimization algorithms are widely used in many industrial machine learning applications. However choosing the appropriate algorithm and cluster size is often difficult…
CYCLADES: Conflict-free Asynchronous Machine Learning
Xinghao Pan, Maximilian Lam, Stephen Tu +6
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and…
Revisiting Distributed Synchronous SGD
Jianmin Chen, Xinghao Pan, Rajat Monga +2
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the c…
Parallel Correlation Clustering on Big Graphs
Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak +3
Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: a…
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos +3
We introduce and analyze stochastic optimization methods where the input to each gradient update is perturbed by bounded noise. We show that this framework forms the basis of a uni…
MLI: An API for Distributed Machine Learning
Evan R. Sparks, Ameet Talwalkar, Virginia Smith +6
MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. I…