Search Results for author: Carlee Joe-Wong

Found 20 papers, 6 papers with code

FedRule: Federated Rule Recommendation System with Graph Neural Networks

no code implementations13 Nov 2022 Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen, Carlee Joe-Wong, Tianqiang Liu

Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door.

Link Prediction Recommendation Systems

pFedDef: Defending Grey-Box Attacks for Personalized Federated Learning

1 code implementation17 Sep 2022 Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong

We are the first to characterize the transferability of such internal evasion attacks for different learning methods and analyze the trade-off between model accuracy and robustness depending on the degree of personalization and similarities in client data.

Adversarial Robustness Personalized Federated Learning +1

Hierarchical Conversational Preference Elicitation with Bandit Feedback

no code implementations6 Sep 2022 Jinhang Zuo, Songwen Hu, Tong Yu, Shuai Li, Handong Zhao, Carlee Joe-Wong

To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories.

Recommendation Systems

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

no code implementations31 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.

Faithful Explanations for Deep Graph Models

no code implementations24 May 2022 Zifan Wang, Yuhang Yao, Chaoran Zhang, Han Zhang, Youjie Kang, Carlee Joe-Wong, Matt Fredrikson, Anupam Datta

Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful.

Anomaly Detection

FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks

1 code implementation28 Jan 2022 Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong

Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated, like GDPR in the EU.

Federated Learning Node Classification

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

no code implementations11 Dec 2021 Yichen Ruan, Carlee Joe-Wong

Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution.

Association Federated Learning

GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs

no code implementations11 Oct 2021 Yucai Fan, Yuhang Yao, Carlee Joe-Wong

These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels.

Classification Graph Representation Learning +1

Combinatorial Multi-armed Bandits for Resource Allocation

1 code implementation10 May 2021 Jinhang Zuo, Carlee Joe-Wong

In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward.

Multi-Armed Bandits

Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

2 code implementations16 Dec 2020 Yuhang Yao, Carlee Joe-Wong

We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.

Graph Clustering Stochastic Block Model

Can we Generalize and Distribute Private Representation Learning?

1 code implementation5 Oct 2020 Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher Brinton

We study the problem of learning representations that are private yet informative, i. e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes.

Federated Learning Privacy Preserving Deep Learning +1

Reconstructing Actions To Explain Deep Reinforcement Learning

no code implementations17 Sep 2020 Xuan Chen, Zifan Wang, Yucai Fan, Bonan Jin, Piotr Mardziel, Carlee Joe-Wong, Anupam Datta

Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.

Atari Games Feature Importance +1

Online Competitive Influence Maximization

no code implementations24 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.

Towards Flexible Device Participation in Federated Learning

no code implementations12 Jun 2020 Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, Carlee Joe-Wong

Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning.

Federated Learning

Network-Aware Optimization of Distributed Learning for Fog Computing

no code implementations17 Apr 2020 Yuwei Tu, Yichen Ruan, Su Wang, Satyavrat Wagle, Christopher G. Brinton, Carlee Joe-Wong

Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.

Distributed, Parallel, and Cluster Computing

Machine Learning on Volatile Instances

no code implementations12 Mar 2020 Xiaoxi Zhang, Jian-Yu Wang, Gauri Joshi, Carlee Joe-Wong

Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes.

BIG-bench Machine Learning

Observe Before Play: Multi-armed Bandit with Pre-observations

no code implementations21 Nov 2019 Jinhang Zuo, Xiaoxi Zhang, Carlee Joe-Wong

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round.

MOVI: A Model-Free Approach to Dynamic Fleet Management

no code implementations13 Apr 2018 Takuma Oda, Carlee Joe-Wong

Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles.


On the Real-time Vehicle Placement Problem

1 code implementation4 Dec 2017 Abhinav Jauhri, Carlee Joe-Wong, John Paul Shen

Motivated by ride-sharing platforms' efforts to reduce their riders' wait times for a vehicle, this paper introduces a novel problem of placing vehicles to fulfill real-time pickup requests in a spatially and temporally changing environment.

online learning

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