49 citations · 228 across the 27 of their papers we have counts for
27 papers
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ling Team, Binwei Zeng, Chao Huang +71
In this technical report, we tackle the challenges of training large-scale Mixture of Experts (MoE) models, focusing on overcoming cost inefficiency and resource limitations preval…
Personalized Binomial DAGs Learning with Network Structured Covariates
Boxin Zhao, Weishi Wang, Dingyuan Zhu +5
The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using…
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
Junjie Wang, Dan Yang, Binbin Hu +3
In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this is…
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
Chunjing Gan, Bo Huang, Binbin Hu +6
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery,…
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
Chunjing Gan, Dan Yang, Binbin Hu +6
Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user pr…
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Sicong Xie, Binbin Hu, Fengze Li +4
Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which gr…
A Practical Distributed ADMM Solver for Billion-Scale Generalized Assignment Problems
Jun Zhou, Feng Qi, Zhigang Hua +5
Assigning items to owners is a common problem found in various real-world applications, for example, audience-channel matching in marketing campaigns, borrower-lender matching in l…
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Jihong Wang, Minnan Luo, Jundong Li +3
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rende…
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Lingxiao Wang, Ziqi Liu +4
Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling…
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Zhibo Zhu, Ziqi Liu, Ge Jin +4
Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are mac…
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
Tianchi Cai, Daxi Cheng, Chen Liang +6
A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B tes…
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang +4
Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computati…
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen, Jun Zhou, Longfei Zheng +7
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between…
AGL: a Scalable System for Industrial-purpose Graph Machine Learning
Dalong Zhang, Xin Huang, Ziqi Liu +8
Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as…
Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
Chaochao Chen, Ziqi Liu, Peilin Zhao +2
Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the exi…
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
Cen Chen, Chen Liang, Jianbin Lin +7
The insurance industry has been creating innovative products around the emerging online shopping activities. Such e-commerce insurance is designed to protect buyers from potential…
DSSLP: A Distributed Framework for Semi-supervised Link Prediction
Dalong Zhang, Xianzheng Song, Ziqi Liu +4
Link prediction is widely used in a variety of industrial applications, such as merchant recommendation, fraudulent transaction detection, and so on. However, it's a great challeng…
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
Ziqi Liu, Dong Wang, Qianyu Yu +8
Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited…
Uncovering Insurance Fraud Conspiracy with Network Learning
Chen Liang, Ziqi Liu, Bin Liu +4
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over…
Heterogeneous Graph Neural Networks for Malicious Account Detection
Ziqi Liu, Chaochao Chen, Xinxing Yang +3
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform. Our…
How Much Can A Retailer Sell? Sales Forecasting on Tmall
Chaochao Chen, Ziqi Liu, Jun Zhou +4
Time-series forecasting is an important task in both academic and industry, which can be applied to solve many real forecasting problems like stock, water-supply, and sales predict…
A Time Attention based Fraud Transaction Detection Framework
Longfei Li, Ziqi Liu, Chaochao Chen +3
With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security…
Model-Protected Multi-Task Learning
Jian Liang, Ziqi Liu, Jiayu Zhou +3
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independent…
Distributed Collaborative Hashing and Its Applications in Ant Financial
Chaochao Chen, Ziqi Liu, Peilin Zhao +3
Collaborative filtering, especially latent factor model, has been popularly used in personalized recommendation. Latent factor model aims to learn user and item latent factors from…