no code implementations • 5 May 2024 • Zhuohua Li, Maoli Liu, John C. S. Lui
These systems interactively present queries associated with "key terms" to users and leverage user feedback to estimate user preferences more efficiently.
no code implementations • 15 Mar 2024 • Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou
We investigate the non-stationary stochastic linear bandit problem where the reward distribution evolves each round.
no code implementations • 26 Feb 2024 • Hantao Yang, Xutong Liu, Zhiyong Wang, Hong Xie, John C. S. Lui, Defu Lian, Enhong Chen
We study the problem of federated contextual combinatorial cascading bandits, where $|\mathcal{U}|$ agents collaborate under the coordination of a central server to provide tailored recommendations to the $|\mathcal{U}|$ corresponding users.
no code implementations • 6 Feb 2024 • Xiaoxin Su, Yipeng Zhou, Laizhong Cui, John C. S. Lui, Jiangchuan Liu
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients.
no code implementations • 3 Nov 2023 • Jinhang Zuo, Zhiyao Zhang, Xuchuang Wang, Cheng Chen, Shuai Li, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game.
no code implementations • 8 Aug 2023 • Lin Yang, Xuchuang Wang, Mohammad Hajiesmaili, Lijun Zhang, John C. S. Lui, Don Towsley
Prior algorithms in both paradigms achieve the optimal group regret.
no code implementations • 14 May 2023 • Behnaz Soltani, Yipeng Zhou, Venus Haghighi, John C. S. Lui
In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server.
no code implementations • 30 Mar 2023 • Xutong Liu, Jinhang Zuo, Siwei Wang, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen
We study contextual combinatorial bandits with probabilistically triggered arms (C$^2$MAB-T) under a variety of smoothness conditions that capture a wide range of applications, such as contextual cascading bandits and contextual influence maximization bandits.
1 code implementation • 1 Mar 2023 • Zhiyong Wang, Xutong Liu, Shuai Li, John C. S. Lui
To tackle these issues, we first propose ``ConLinUCB", a general framework for conversational bandits with better information incorporation, combining arm-level and key-term-level feedback to estimate user preference in one step at each time.
no code implementations • 15 Feb 2023 • Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad Hajiesmaili, John C. S. Lui, Don Towsley
We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times.
no code implementations • 28 Jan 2023 • Liming Wang, Hong Xie, Ye Li, Jian Tan, John C. S. Lui
Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications.
no code implementations • 12 Nov 2022 • Russell Lee, Bo Sun, Mohammad Hajiesmaili, John C. S. Lui
This paper develops learning-augmented algorithms for energy trading in volatile electricity markets.
no code implementations • 31 Aug 2022 • Xutong Liu, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C. S. Lui, Wei Chen
Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications.
1 code implementation • 31 Aug 2022 • Xutong Liu, Haoru Zhao, Tong Yu, Shuai Li, John C. S. Lui
Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems.
no code implementations • 17 Jun 2022 • Xuchuang Wang, Hong Xie, John C. S. Lui
When the "per-load" reward follows a Gaussian distribution, we prove a sample complexity lower bound of learning the capacity from load-dependent rewards and also a regret lower bound of this new MP-MAB problem.
no code implementations • 5 May 2022 • Zhi Qi, Hong Xie, Ye Li, Jian Tan, Feifei Li, John C. S. Lui
LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc.
no code implementations • 28 Apr 2022 • Xuchuang Wang, Hong Xie, John C. S. Lui
The reward from a shareable arm is equal to the "per-load" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources.
no code implementations • 22 Dec 2021 • Xiong Wang, Jiancheng Ye, John C. S. Lui
Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements.
no code implementations • 20 Dec 2021 • Xuanjie Li, Yuedong Xu, Jessie Hui Wang, Xin Wang, John C. S. Lui
By transforming our decentralized algorithm into a centralized inexact proximal gradient algorithm with variance reduction, and controlling the bounds of error sequences, we prove that DPSVRG converges at the rate of $O(1/T)$ for general convex objectives plus a non-smooth term with $T$ as the number of iterations, while DSPG converges at the rate $O(\frac{1}{\sqrt{T}})$.
no code implementations • NeurIPS 2021 • Lin Yang, Yu-Zhen Janice Chen, Stephen Pasteris, Mohammad Hajiesmaili, John C. S. Lui, Don Towsley
This paper studies a cooperative multi-armed bandit problem with $M$ agents cooperating together to solve the same instance of a $K$-armed stochastic bandit problem with the goal of maximizing the cumulative reward of agents.
no code implementations • 9 Jun 2021 • Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C. S. Lui
For the online learning setting, neither the network structure nor the node weights are known initially.
no code implementations • NeurIPS 2020 • Lin Yang, Mohammad Hajiesmaili, Mohammad Sadegh Talebi, John C. S. Lui, Wing Shing Wong
We characterize the regret of ExpRb as a function of the corruption budget and show that for the case of a known corruption budget, the regret of ExpRb is tight.
no code implementations • NeurIPS 2020 • Siwei Wang, Longbo Huang, John C. S. Lui
Compared to existing algorithms, our result eliminates the exponential factor (in $M, N$) in the regret upper bound, due to a novel exploitation of the sparsity in transitions in general restless bandit problems.
no code implementations • 24 Jun 2020 • Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C. S. Lui, Wei Chen
In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e. g., products, news stories) propagate in the same network and influence probabilities on edges are unknown.
1 code implementation • 2020 • Yongkun Li, Zhiyong Wu, Shuai Lin, Hong Xie, Min Lv, Yinlong Xu, John C. S. Lui
Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis.
no code implementations • 16 Jan 2020 • Li Ye, Yishi Lin, Hong Xie, John C. S. Lui
A typical alternative is offline causal inference, which analyzes logged data alone to make decisions.
no code implementations • 4 Jun 2019 • Xiaoying Zhang, Hong Xie, Hang Li, John C. S. Lui
Here, a key-term can relate to a subset of arms, for example, a category of articles in news recommendation.
no code implementations • NeurIPS 2018 • Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui
We introduce the community exploration problem that has many real-world applications such as online advertising.
1 code implementation • 7 Oct 2018 • Xutong Liu, Yu-Zhen Janice Chen, John C. S. Lui, Konstantin Avrachenkov
The number of each graphlet, called graphlet count, is a signature which characterizes the local network structure of a given graph.
no code implementations • 4 May 2018 • Kun Chen, Kechao Cai, Longbo Huang, John C. S. Lui
The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e. g., advertisements, recommendations, or news feeds.
no code implementations • 8 Sep 2017 • Kechao Cai, Kun Chen, Longbo Huang, John C. S. Lui
To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable \emph{sub-linear} regret and violation bounds.
no code implementations • 10 Dec 2016 • Mingshen Sun, Xiaolei Li, John C. S. Lui, Richard T. B. Ma, Zhenkai Liang
We present the design and implementation of MONET, which has a client and a backend server module.
Cryptography and Security
no code implementations • 28 Feb 2013 • Min Zheng, Mingshen Sun, John C. S. Lui
In this paper, we present the design and implementation of DroidAnalytics, a signature based analytic system to automatically collect, manage, analyze and extract android malware.
Cryptography and Security
1 code implementation • 18 Sep 2011 • Jun He, Laura Balzano, John C. S. Lui
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information.