Search Results for author: Jian Xu

Found 85 papers, 24 papers with code

Robust Representation Learning for Unified Online Top-K Recommendation

no code implementations24 Oct 2023 Minfang Lu, Yuchen Jiang, Huihui Dong, Qi Li, Ziru Xu, Yuanlin Liu, Lixia Wu, Haoyuan Hu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng

The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.

Fairness Representation Learning

Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction

no code implementations16 Oct 2023 Gang Zhao, Yidong Shi, Shudong Lu, Xinjie Yang, Guanting Dong, Jian Xu, Xiaocheng Gong, Si Li

Although previous methods attempt to address these challenges, they overlook the interference of event-unrelated sentences during event detection and neglect the mutual interference of different event roles during argument extraction.

Document-level Event Extraction Event Detection +1

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes

no code implementations22 Sep 2023 Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng

Furthermore, our method guarantees theoretically controlled prediction error for DGP models and demonstrates remarkable performance on various datasets.

Bayesian Inference Gaussian Processes +2

Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning

no code implementations17 Sep 2023 Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, John Paisley, Delu Zeng

To address this limitation, we incorporate normalizing flows to reparameterize the vector field of ODEs, resulting in a more flexible and expressive prior distribution.

Bayesian Inference Gaussian Processes +2

CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

no code implementations ICCV 2023 Kailin Li, Lixin Yang, Haoyu Zhen, Zenan Lin, Xinyu Zhan, Licheng Zhong, Jian Xu, Kejian Wu, Cewu Lu

This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located.

Object Reconstruction

Multi-Scenario Ranking with Adaptive Feature Learning

no code implementations29 Jun 2023 Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.

Retrieval Transfer Learning

Personalized Federated Learning with Feature Alignment and Classifier Collaboration

3 code implementations20 Jun 2023 Jian Xu, Xinyi Tong, Shao-Lun Huang

Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients.

Personalized Federated Learning Representation Learning

COPR: Consistency-Oriented Pre-Ranking for Online Advertising

no code implementations6 Jun 2023 Zhishan Zhao, Jingyue Gao, Yu Zhang, Shuguang Han, Siyuan Lou, Xiang-Rong Sheng, Zhe Wang, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng

In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements.

POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo

1 code implementation CVPR 2023 Lixin Yang, Jian Xu, Licheng Zhong, Xinyu Zhan, Zhicheng Wang, Kejian Wu, Cewu Lu

Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks.

Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout

no code implementations11 Mar 2023 Jian Xu, Meiling Yang, Wenbo Ding, Shao-Lun Huang

The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly developed for collaborative model training over decentralized data sources while preserving user privacy.

Federated Learning

Sustainable Online Reinforcement Learning for Auto-bidding

1 code implementation13 Oct 2022 Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, Bo Zheng

Due to safety concerns, it was believed that the RL training process can only be carried out in an offline virtual advertising system (VAS) that is built based on the historical data generated in the RAS.

Q-Learning reinforcement-learning +1

Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model

no code implementations12 Aug 2022 Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng, Siran Yang, Shuguang Han, Hongbo Deng, Yuning Jiang, Jian Xu, Bo Zheng

It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.

Click-Through Rate Prediction

FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval

no code implementations12 Jul 2022 Meilin Yang, Jian Xu, Yang Liu, Wenbo Ding

To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train the shared deep hashing model by leveraging the prototypical hash codes for each class.

Deep Hashing Federated Learning

Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization

no code implementations2 Jun 2022 Mingyuan Cheng, Xinru Liao, Quan Liu, Bin Ma, Jian Xu, Bo Zheng

Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies.

Causal Inference counterfactual +3

Hierarchically Constrained Adaptive Ad Exposure in Feeds

no code implementations31 May 2022 Dagui Chen, Qi Yan, Chunjie Chen, Zhenzhe Zheng, Yangsu Liu, Zhenjia Ma, Chuan Yu, Jian Xu, Bo Zheng

To this end, adaptive ad exposure has become an appealing strategy to boost the overall performance of the feed.

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model

no code implementations23 May 2022 Wenbo Su, Yuanxing Zhang, Yufeng Cai, Kaixu Ren, Pengjie Wang, Huimin Yi, Yue Song, Jing Chen, Hongbo Deng, Jian Xu, Lin Qu, Bo Zheng

High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models.

Recommendation Systems

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

no code implementations4 Apr 2022 Zhaoxin Fan, Zhenbo Song, Jian Xu, Zhicheng Wang, Kejian Wu, Hongyan Liu, Jun He

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications.

6D Pose Estimation using RGB

APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction

1 code implementation30 Mar 2022 Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Hongbo Deng, Jian Xu, Bo Zheng

In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted.

Click-Through Rate Prediction

AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System

no code implementations28 Mar 2022 Zhirong Xu, Shiyang Wen, Junshan Wang, Guojun Liu, Liang Wang, Zhi Yang, Lei Ding, Yan Zhang, Di Zhang, Jian Xu, Bo Zheng

Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.

Graph Embedding Information Retrieval +1

TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed

1 code implementation CVPR 2022 Shian Du, Yihong Luo, Wei Chen, Jian Xu, Delu Zeng

In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training.

Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface

no code implementations16 Mar 2022 Diu Khue Luu, Anh Tuan Nguyen, Ming Jiang, Markus W. Drealan, Jian Xu, Tong Wu, Wing-kin Tam, Wenfeng Zhao, Brian Z. H. Lim, Cynthia K. Overstreet, Qi Zhao, Jonathan Cheng, Edward W. Keefer, Zhi Yang

Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines.

Impression Allocation and Policy Search in Display Advertising

no code implementations11 Mar 2022 Di wu, Cheng Chen, Xiujun Chen, Junwei Pan, Xun Yang, Qing Tan, Jian Xu, Kuang-Chih Lee

In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable.

Multi-agent Reinforcement Learning

Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval

1 code implementation7 Mar 2022 Dingkun Long, Qiong Gao, Kuan Zou, Guangwei Xu, Pengjun Xie, Ruijie Guo, Jian Xu, Guanjun Jiang, Luxi Xing, Ping Yang

We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain.

Passage Retrieval Retrieval

Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction

1 code implementation14 Feb 2022 Yu Chen, Jiaqi Jin, Hui Zhao, Pengjie Wang, Guojun Liu, Jian Xu, Bo Zheng

Moreover, to estimate CVR upon the freshly observed but biased distribution with fake negatives, the importance sampling is widely used to reduce the distribution bias.

Learning Continuous Face Representation with Explicit Functions

no code implementations25 Oct 2021 Liping Zhang, Weijun Li, Linjun Sun, Lina Yu, Xin Ning, Xiaoli Dong, Jian Xu, Hong Qin

First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element.

Denoising Image Restoration

Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering

3 code implementations13 Sep 2021 Jian Xu, Shao-Lun Huang, Linqi Song, Tian Lan

To this end, previous work either makes use of auxiliary data at parameter server to verify the received gradients (e. g., by computing validation error rate) or leverages statistic-based methods (e. g. median and Krum) to identify and remove malicious gradients from Byzantine clients.

Federated Learning Model Poisoning +2

Binary Code based Hash Embedding for Web-scale Applications

no code implementations24 Aug 2021 Bencheng Yan, Pengjie Wang, Jinquan Liu, Wei Lin, Kuang-Chih Lee, Jian Xu, Bo Zheng

In these applications, embedding learning of categorical features is crucial to the success of deep learning models.

Recommendation Systems

Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

no code implementations8 Jun 2021 Ziyu Guan, Hongchang Wu, Qingyu Cao, Hao liu, Wei Zhao, Sheng Li, Cai Xu, Guang Qiu, Jian Xu, Bo Zheng

Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.

Model Optimization Multi-agent Reinforcement Learning

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising

no code implementations7 Jun 2021 Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, YiQing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.

Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction

no code implementations17 May 2021 Eric Z. Chen, Xiao Chen, Jingyuan Lyu, Qi Liu, Zhongqi Zhang, Yu Ding, Shuheng Zhang, Terrence Chen, Jian Xu, Shanhui Sun

To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.

Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning

no code implementations17 May 2021 Eric Z. Chen, Yongquan Ye, Xiao Chen, Jingyuan Lyu, Zhongqi Zhang, Yichen Hu, Terrence Chen, Jian Xu, Shanhui Sun

We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.

MRI Reconstruction

A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control

no code implementations24 Mar 2021 Anh Tuan Nguyen, Markus W. Drealan, Diu Khue Luu, Ming Jiang, Jian Xu, Jonathan Cheng, Qi Zhao, Edward W. Keefer, Zhi Yang

This enables the implementation of the neuroprosthetic hand as a portable and self-contained unit with real-time control of individual finger movements.


Computation Resource Allocation Solution in Recommender Systems

no code implementations3 Mar 2021 Xun Yang, Yunli Wang, Cheng Chen, Qing Tan, Chuan Yu, Jian Xu, Xiaoqiang Zhu

On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.

Recommendation Systems

Calibrating User Response Predictions in Online Advertising

no code implementations PKKDD 2020 Chao Deng, Hao Wang, Qing Tan, Jian Xu, and Kun Gai

Due to the sparsity and latency of the user response behaviors such as clicks and conversions, traditional calibration methods may not work well in real-world online advertising systems.

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

no code implementations5 Dec 2020 Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.

Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

1 code implementation25 Nov 2020 Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee

In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.

Click-Through Rate Prediction Gaussian Processes

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

1 code implementation21 Sep 2020 Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu

Conventional nonlinear subspace learning techniques (e. g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination).

Dimensionality Reduction

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

no code implementations20 Aug 2020 Liyi Guo, Rui Lu, Haoqi Zhang, Junqi Jin, Zhenzhe Zheng, Fan Wu, Jin Li, Haiyang Xu, Han Li, Wenkai Lu, Jian Xu, Kun Gai

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.


Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network

no code implementations12 Aug 2020 Eric Z. Chen, Xiao Chen, Jingyuan Lyu, Yuan Zheng, Terrence Chen, Jian Xu, Shanhui Sun

Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.

Image Reconstruction

GmFace: A Mathematical Model for Face Image Representation Using Multi-Gaussian

no code implementations3 Aug 2020 Liping Zhang, Weijun Li, Lina Yu, Xiaoli Dong, Linjun Sun, Xin Ning, Jian Xu, Hong Qin

The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet.

Face Model

Learning Optimal Tree Models Under Beam Search

1 code implementation ICML 2020 Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.

Information Retrieval Recommendation Systems +1

Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising

no code implementations9 May 2020 Xiaotian Hao, Junqi Jin, Jianye Hao, Jin Li, Weixun Wang, Yi Ma, Zhenzhe Zheng, Han Li, Jian Xu, Kun Gai

Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 Apr 2020 Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.

Information Retrieval Retrieval +1

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

1 code implementation2 May 2019 Kan Ren, Jiarui Qin, Yuchen Fang, Wei-Nan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai

In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.


Learning Adaptive Display Exposure for Real-Time Advertising

no code implementations10 Sep 2018 Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Wei-Nan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai

In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?

A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising

no code implementations10 Sep 2018 Di Wu, Cheng Chen, Xun Yang, Xiujun Chen, Qing Tan, Jian Xu, Kun Gai

With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.

Multi-agent Reinforcement Learning reinforcement-learning +1

Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

no code implementations ECCV 2018 Danfeng Hong, Naoto Yokoya, Jian Xu, Xiaoxiang Zhu

Despite the fact that nonlinear subspace learning techniques (e. g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization).

General Classification Multi-Label Classification +1

Excavate Condition-invariant Space by Intrinsic Encoder

no code implementations29 Jun 2018 Jian Xu, Chunheng Wang, Cunzhao Shi, Baihua Xiao

Our method excavates the space of intrinsic structure and semantic information by proposed self-supervised encoder loss.

Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

4 code implementations9 Jun 2018 Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu

The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.

Click-Through Rate Prediction

Locally Adaptive Learning Loss for Semantic Image Segmentation

no code implementations23 Feb 2018 Jinjiang Guo, Pengyuan Ren, Aiguo Gu, Jian Xu, Weixin Wu

We propose a novel locally adaptive learning estimator for enhancing the inter- and intra- discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks.

Image Segmentation Segmentation +1

Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

no code implementations23 Feb 2018 Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, Kun Gai

Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.

Marketing reinforcement-learning +1

Advancing System Performance with Redundancy: From Biological to Artificial Designs

no code implementations14 Feb 2018 Anh Tuan Nguyen, Jian Xu, Diu Khue Luu, Qi Zhao, Zhi Yang

We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems as well as assist the studies of the fundamental mechanisms governing various biological processes.

Detecting Anomalies in Sequential Data with Higher-order Networks

1 code implementation27 Dec 2017 Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla

A major branch of anomaly detection methods relies on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure.

Social and Information Networks Physics and Society

Semantic Frame Labeling with Target-based Neural Model

no code implementations SEMEVAL 2017 Yukun Feng, Dong Yu, Jian Xu, Chunhua Liu

This paper explores the automatic learning of distributed representations of the target{'}s context for semantic frame labeling with target-based neural model.

Feature Engineering Word Embeddings +1

Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval

1 code implementation14 Jul 2017 Jian Xu, Chunheng Wang, Chengzuo Qi, Cunzhao Shi, Baihua Xiao

Without post-processing, Our IME layer achieves a boost in performance of state-of-the-art image retrieval methods with post-processing on most datasets, and needs less computational cost.

Dimensionality Reduction Image Retrieval +1

Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval

1 code implementation3 May 2017 Jian Xu, Cunzhao Shi, Chengzuo Qi, Chunheng Wang, Baihua Xiao

In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval.

Image Retrieval Retrieval

Two-View Label Propagation to Semi-supervised Reader Emotion Classification

no code implementations COLING 2016 Shoushan Li, Jian Xu, Dong Zhang, Guodong Zhou

In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm.

Classification Emotion Classification +2

Lift-Based Bidding in Ad Selection

no code implementations17 Jul 2015 Jian Xu, Xuhui Shao, Jianjie Ma, Kuang-Chih Lee, Hang Qi, Quan Lu

In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events.


Smart Pacing for Effective Online Ad Campaign Optimization

no code implementations18 Jun 2015 Jian Xu, Kuang-Chih Lee, Wentong Li, Hang Qi, Quan Lu

In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals.

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