29 citations · 220 across the 30 of their papers we have counts for
13 papers · 1 filter
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
Ergute Bao, Yizheng Zhu, Xiaokui Xiao +4
Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train…
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
Haonan Wang, Jieyu Zhang, Qi Zhu +3
Existing graph contrastive learning (GCL) techniques typically require two forward passes for a single instance to construct the contrastive loss, which is effective for capturing…
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi +1
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses o…
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling
Jianxin Wei, Ergute Bao, Xiaokui Xiao +1
Nowadays, differential privacy (DP) has become a well-accepted standard for privacy protection, and deep neural networks (DNN) have been immensely successful in machine learning. T…
An Embedding-Based Grocery Search Model at Instacart
Yuqing Xie, Taesik Na, Xiao Xiao +7
The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system le…
Measuring Friendship Closeness: A Perspective of Social Identity Theory
Shiqi Zhang, Jiachen Sun, Wenqing Lin +2
Measuring the closeness of friendships is an important problem that finds numerous applications in practice. For example, online gaming platforms often host friendship-enhancing ev…
GlassDB: An Efficient Verifiable Ledger Database System Through Transparency
Cong Yue, Tien Tuan Anh Dinh, Zhongle Xie +4
Verifiable ledger databases protect data history against malicious tampering. Existing systems, such as blockchains and certificate transparency, are based on transparency logs --…
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
Tianyuan Jin, Pan Xu, Xiaokui Xiao +1
We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers ma…
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?
Héber H. Arcolezi, Jean-François Couchot, Denis Renaud +2
This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-o…
The Metaverse Data Deluge: What Can We Do About It?
Beng Chin Ooi, Gang Chen, Mike Zheng Shou +5
In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space…
Fast Quantum Calibration using Bayesian Optimization with State Parameter Estimator for Non-Markovian Environment
Peng Qian, Shahid Qamar, Xiao Xiao +5
As quantum systems expand in size and complexity, manual qubit characterization and gate optimization will be a non-scalable and time-consuming venture. Physical qubits have to be…
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
Juncheng Liu, Zequn Sun, Bryan Hooi +5
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of…
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Juncheng Liu, Kenji Kawaguchi, Bryan Hooi +2
Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN mo…