35 citations · 133 across the 19 of their papers we have counts for
19 papers
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Zhuqing Liu, Xin Zhang, Prashant Khanduri +2
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in mo…
A global convergence theory for deep ReLU implicit networks via over-parameterization
Tianxiang Gao, Hailiang Liu, Jia Liu +2
Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectu…
Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri +1
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabiliti…
Toward Efficient Online Scheduling for Distributed Machine Learning Systems
Menglu Yu, Jia Liu, Chuan Wu +2
Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. How…
Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach
Fengjiao Li, Jia Liu, Bo Ji
In this paper, we study the problem of fair worker selection in Federated Learning systems, where fairness serves as an incentive mechanism that encourages more workers to particip…
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning
Haibo Yang, Jia Liu, Elizabeth S. Bentley
Federated learning (FL) is a prevailing distributed learning paradigm, where a large number of workers jointly learn a model without sharing their training data. However, high comm…
Incentivized Bandit Learning with Self-Reinforcing User Preferences
Tianchen Zhou, Jia Liu, Chaosheng Dong +1
In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pu…
A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource Scheduling
Menglu Yu, Chuan Wu, Bo Ji +1
In recent years, to sustain the resource-intensive computational needs for training deep neural networks (DNNs), it is widely accepted that exploiting the parallelism in large-scal…
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementat…
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Haibo Yang, Minghong Fang, Jia Liu
Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received in…
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu +1
Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. The key idea of existing Byz…
Multi-Armed Bandits with Local Differential Privacy
Wenbo Ren, Xingyu Zhou, Jia Liu +1
This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the…
The Sample Complexity of Best- Items Selection from Pairwise Comparisons
Wenbo Ren, Jia Liu, Ness B. Shroff
This paper studies the sample complexity (aka number of comparisons) bounds for the active best- items selection from pairwise comparisons. From a given set of items, the learne…
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Peizhong Ju, Xiaojun Lin, Jia Liu
Recently, there have been significant interests in studying the so-called "double-descent" of the generalization error of linear regression models under the overparameterized and o…
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
Wenbo Ren, Jia Liu, Ness B. Shroff
This paper studies the problem of finding the exact ranking from noisy comparisons. A comparison over a set of items produces a noisy outcome about the most preferred item, and…
Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
Xin Zhang, Jia Liu, Zhengyuan Zhu
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the netw…
Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?
Bin Li, Jia Liu
The proliferation of smart mobile devices has spurred an explosive growth of mobile crowd-learning services, where service providers rely on the user community to voluntarily colle…
Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Network consensus optimization has received increasing attention in recent years and has found important applications in many scientific and engineering fields. To solve network co…