Search Results for author: Yuan Qi

Found 42 papers, 8 papers with code

Temporal Logic Point Processes

no code implementations ICML 2020 Shuang Li, Lu Wang, Ruizhi Zhang, xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song

We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.

Point Processes

SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing

no code implementations3 Jul 2021 Hui Li, Xing Fu, Ruofan Wu, Jinyu Xu, Kai Xiao, xiaofu Chang, Weiqiang Wang, Shuai Chen, Leilei Shi, Tao Xiong, Yuan Qi

Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.

Product Recommendation

Practical Locally Private Federated Learning with Communication Efficiency

no code implementations1 Jan 2021 Yan Feng, Tao Xiong, Ruofan Wu, Yuan Qi

We also initialize a discussion about the role of quantization and perturbation in FL algorithm design with privacy and communication constraints.

Federated Learning Quantization

Memory Augmented Design of Graph Neural Networks

no code implementations1 Jan 2021 Tao Xiong, Liang Zhu, Ruofan Wu, Yuan Qi

Specifically, we allow every node in the original graph to interact with a group of memory nodes.

Node Classification

Question Directed Graph Attention Network for Numerical Reasoning over Text

no code implementations EMNLP 2020 Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.

Graph Attention Machine Reading Comprehension +2

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

no code implementations8 Aug 2020 Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He

In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.

Fraud Detection

Bandit Samplers for Training Graph Neural Networks

2 code implementations NeurIPS 2020 Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi

However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).

Graph Attention

A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning

no code implementations19 May 2020 Shijun Wang, Baocheng Zhu, Lintao Ma, Yuan Qi

In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints.

Metric Learning

Riemannian Proximal Policy Optimization

no code implementations19 May 2020 Shijun Wang, Baocheng Zhu, Chen Li, Mingzhe Wu, James Zhang, Wei Chu, Yuan Qi

In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems.

SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check

1 code implementation ACL 2020 Xingyi Cheng, Weidi Xu, Kunlong Chen, Shaohua Jiang, Feng Wang, Taifeng Wang, Wei Chu, Yuan Qi

This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).

Variational Policy Propagation for Multi-agent Reinforcement Learning

no code implementations19 Apr 2020 Chao Qu, Hui Li, Chang Liu, Junwu Xiong, James Zhang, Wei Chu, Weiqiang Wang, Yuan Qi, Le Song

We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay

no code implementations1 Apr 2020 Jianbin Lin, Zhiqiang Zhang, Jun Zhou, Xiaolong Li, Jingli Fang, Yanming Fang, Quan Yu, Yuan Qi

Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.

Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition

no code implementations12 Mar 2020 Zhigang Dai, Jinhua Fu, Qile Zhu, Hengbin Cui, Xiaolong Li, Yuan Qi

We revise the attention distribution to focus on the local and contextual semantic information by incorporating the relative position information between utterances.

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

no code implementations10 Mar 2020 Yankun Ren, Jianbin Lin, Siliang Tang, Jun Zhou, Shuang Yang, Yuan Qi, Xiang Ren

It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.

Adversarial Text General Classification +3

RNE: A Scalable Network Embedding for Billion-scale Recommendation

no code implementations10 Mar 2020 Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, Yuan Qi

However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.

Network Embedding

Practical Privacy Preserving POI Recommendation

no code implementations5 Mar 2020 Chaochao Chen, Jun Zhou, Bingzhe Wu, Wenjin Fang, Li Wang, Yuan Qi, Xiaolin Zheng

Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.

Federated Learning

Long Short-Term Sample Distillation

no code implementations2 Mar 2020 Liang Jiang, Zujie Wen, Zhongping Liang, Yafang Wang, Gerard de Melo, Zhe Li, Liangzhuang Ma, Jiaxing Zhang, Xiaolong Li, Yuan Qi

The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

1 code implementation28 Feb 2020 Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi

Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.

Fraud Detection

How Much Can A Retailer Sell? Sales Forecasting on Tmall

no code implementations27 Feb 2020 Chaochao Chen, Ziqi Liu, Jun Zhou, Xiaolong Li, Yuan Qi, Yujing Jiao, Xingyu Zhong

By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).

Time Series Time Series Forecasting

Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing

no code implementations27 Feb 2020 Ziqi Liu, Dong Wang, Qianyu Yu, Zhiqiang Zhang, Yue Shen, Jian Ma, Wenliang Zhong, Jinjie Gu, Jun Zhou, Shuang Yang, Yuan Qi

In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.

Graph Representation Learning

Uncovering Insurance Fraud Conspiracy with Network Learning

no code implementations27 Feb 2020 Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi

In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.

Fraud Detection Graph Learning

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

1 code implementation ICLR 2020 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.

Variational Inference

Deep Interaction Processes for Time-Evolving Graphs

no code implementations25 Sep 2019 xiaofu Chang, Jianfeng Wen, Xuqin Liu, Yanming Fang, Le Song, Yuan Qi

To model the dependency between latent dynamic representations of each node, we define a mixture of temporal cascades in which a node's neural representation depends on not only this node's previous representations but also the previous representations of related nodes that have interacted with this node.

TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

no code implementations18 Jun 2019 Shaosheng Cao, Xinxing Yang, Cen Chen, Jun Zhou, Xiaolong Li, Yuan Qi

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.

Fraud Detection

Can Graph Neural Networks Help Logic Reasoning?

no code implementations5 Jun 2019 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

no code implementations7 Feb 2019 Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael. I. Jordan, Yuan Qi, Le Song

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback.

Counterfactual Inference Domain Adaptation

Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

no code implementations NeurIPS 2019 Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong

To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.

Multi-agent Reinforcement Learning reinforcement-learning

Double Neural Counterfactual Regret Minimization

no code implementations ICLR 2020 Hui Li, Kailiang Hu, Zhibang Ge, Tao Jiang, Yuan Qi, Le Song

Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG).

Reinforcement Learning for Uplift Modeling

1 code implementation26 Nov 2018 Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong

Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.

reinforcement-learning

Latent Dirichlet Allocation for Internet Price War

no code implementations23 Aug 2018 Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong

Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

3 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

EigenGP: Gaussian Process Models with Adaptive Eigenfunctions

1 code implementation2 Jan 2014 Hao Peng, Yuan Qi

In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary elements--eigenfunctions of a GP prior--and prior precisions in a sparse finite model.

Gaussian Processes

DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements

no code implementations12 Nov 2013 Shandian Zhe, Yuan Qi, Youngja Park, Ian Molloy, Suresh Chari

To overcome this limitation, we present Distributed Infinite Tucker (DINTUCKER), a large-scale nonlinear tensor decomposition algorithm on MAPREDUCE.

Tensor Decomposition Variational Inference

Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis

no code implementations26 Apr 2013 Shandian Zhe, Zenglin Xu, Yuan Qi

To unify these two tasks, we present a new sparse Bayesian approach for joint association study and disease diagnosis.

Multiview Learning

t-divergence Based Approximate Inference

no code implementations NeurIPS 2011 Nan Ding, Yuan Qi, S. V. N. Vishwanathan

Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions.

EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning

no code implementations NeurIPS 2011 Feng Yan, Yuan Qi

To overcome this limitation, we present a novel hybrid model, EigenNet, that uses the eigenstructures of data to guide variable selection.

Sparse Learning Variable Selection

Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units

no code implementations NeurIPS 2009 Feng Yan, Ningyi Xu, Yuan Qi

Extensive experiments showed that our parallel inference methods consistently produced LDA models with the same predictive power as sequential training methods did but with 26x speedup for CGS and 196x speedup for CVB on a GPU with 30 multiprocessors; actually the speedup is almost linearly scalable with the number of multiprocessors available.

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