399 citations · 3.4k across the 95 of their papers we have counts for
8 papers · 1 filter
Prognostics of Surgical Site Infections using Dynamic Health Data
Chuyang Ke, Yan Jin, Heather Evans +4
Surgical Site Infection (SSI) is a national priority in healthcare research. Much research attention has been attracted to develop better SSI risk prediction models. However, most…
GaDei: On Scale-up Training As A Service For Deep Learning
Wei Zhang, Minwei Feng, Yunhui Zheng +8
Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who hav…
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
Hantian Zhang, Jerry Li, Kaan Kara +3
Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order…
Finite-sum Composition Optimization via Variance Reduced Gradient Descent
Xiangru Lian, Mengdi Wang, Ji Liu
The stochastic composition optimization proposed recently by Wang et al. [2014] minimizes the objective with the compositional expectation form: $\min_x~(\mathbb{E}_iF_i \circ \mat…
A Scalable Algorithm for Structure Identification of Complex Gene Regulatory Network from Temporal Expression Data
Shupeng Gui, Rui Chen, Liang Wu +2
Motivation: Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claime…
Infinite-Label Learning with Semantic Output Codes
Yang Zhang, Rupam Acharyya, Ji Liu +1
We develop a new statistical machine learning paradigm, named infinite-label learning, to annotate a data point with more than one relevant labels from a candidate set, which pools…
Accelerating Stochastic Composition Optimization
Mengdi Wang, Ji Liu, Ethan X. Fang
Consider the stochastic composition optimization problem where the objective is a composition of two expected-value functions. We propose a new stochastic first-order method, namel…
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order
Xiangru Lian, Huan Zhang, Cho-Jui Hsieh +2
Asynchronous parallel optimization received substantial successes and extensive attention recently. One of core theoretical questions is how much speedup (or benefit) the asynchron…