Search Results for author: Yong Yu

Found 104 papers, 52 papers with code

Nested Named Entity Recognition with Span-level Graphs

no code implementations ACL 2022 Juncheng Wan, Dongyu Ru, Weinan Zhang, Yong Yu

In this work, we try to improve the span representation by utilizing retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features.

NER Nested Named Entity Recognition

Multi-Level Interaction Reranking with User Behavior History

1 code implementation20 Apr 2022 Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.

Recommendation Systems

Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization

no code implementations4 Mar 2022 Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang

Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.

Imitation Learning Transfer Learning

Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection

1 code implementation25 Feb 2022 Ting Long, Yutong Xie, Xianyu Chen, Weinan Zhang, Qinxiang Cao, Yong Yu

We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP.

Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation

no code implementations9 Feb 2022 Jiarui Jin, Xianyu Chen, Yuanbo Chen, Weinan Zhang, Renting Rui, Zaifan Jiang, Zhewen Su, Yong Yu

With the prevalence of live broadcast business nowadays, a new type of recommendation service, called live broadcast recommendation, is widely used in many mobile e-commerce Apps.

Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data

no code implementations7 Feb 2022 Jiarui Jin, Xianyu Chen, Weinan Zhang, JunJie Huang, Ziming Feng, Yong Yu

More concretely, we first design a search-based module to retrieve a user's relevant historical behaviors, which are then mixed up with her recent records to be fed into a time-aware sequential network for capturing her time-sensitive demands.

Click-Through Rate Prediction

Efficient Policy Space Response Oracles

no code implementations28 Jan 2022 Ming Zhou, Jingxiao Chen, Ying Wen, Weinan Zhang, Yaodong Yang, Yong Yu

Policy Space Response Oracle method (PSRO) provides a general solution to Nash equilibrium in two-player zero-sum games but suffers from two problems: (1) the computation inefficiency due to consistently evaluating current populations by simulations; and (2) the exploration inefficiency due to learning best responses against a fixed meta-strategy at each iteration.

Efficient Exploration

Generative Adversarial Exploration for Reinforcement Learning

no code implementations27 Jan 2022 Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng Sun

Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel.

Montezuma's Revenge reinforcement-learning

Phrase-level Adversarial Example Generation for Neural Machine Translation

no code implementations6 Jan 2022 Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, Furu Wei

In this paper, we propose a phrase-level adversarial example generation (PAEG) method to enhance the robustness of the model.

Machine Translation Translation

QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

no code implementations16 Nov 2021 Handong Ma, Jiawei Hou, Chenxu Zhu, Weinan Zhang, Ruiming Tang, Jincai Lai, Jieming Zhu, Xiuqiang He, Yong Yu

Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search results.

Question Answering Semantic Similarity +1

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

1 code implementation5 Nov 2021 Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.

Click-Through Rate Prediction

Context-aware Reranking with Utility Maximization for Recommendation

no code implementations18 Oct 2021 Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, Yong Yu

As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.

Graph Attention Recommendation Systems

Why Propagate Alone? Parallel Use of Labels and Features on Graphs

no code implementations ICLR 2022 Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf

In this regard, it has recently been proposed to use a randomly-selected portion of the training labels as GNN inputs, concatenated with the original node features for making predictions on the remaining labels.

Node Property Prediction

Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning

no code implementations ICLR 2022 Jiarui Jin, Sijin Zhou, Weinan Zhang, Tong He, Yong Yu, Rasool Fakoor

Goal-oriented Reinforcement Learning (GoRL) is a promising approach for scaling up RL techniques on sparse reward environments requiring long horizon planning.

Continuous Control graph construction +1

Inductive Relation Prediction Using Analogy Subgraph Embeddings

no code implementations ICLR 2022 Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, Quan Gan

Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i. e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training.

Inductive Relation Prediction

Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction

no code implementations16 Aug 2021 Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Wei-Wei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning

To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.

Diabetes Prediction Multi-Task Learning

Retrieval & Interaction Machine for Tabular Data Prediction

1 code implementation11 Aug 2021 Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, Yong Yu

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.

Click-Through Rate Prediction Recommendation Systems

Learning to Select Cuts for Efficient Mixed-Integer Programming

no code implementations28 May 2021 Zeren Huang, Kerong Wang, Furui Liu, Hui-Ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang

In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.

Multiple Instance Learning

MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

1 code implementation13 May 2021 Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.


An Adversarial Imitation Click Model for Information Retrieval

1 code implementation13 Apr 2021 Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, Yong Yu

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.

Imitation Learning Information Retrieval +1

Bag of Tricks for Node Classification with Graph Neural Networks

1 code implementation24 Mar 2021 Yangkun Wang, Jiarui Jin, Weinan Zhang, Yong Yu, Zheng Zhang, David Wipf

Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.

Classification General Classification +2

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 Jan 2021 Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.

Algorithmic Trading reinforcement-learning

Explore with Dynamic Map: Graph Structured Reinforcement Learning

no code implementations1 Jan 2021 Jiarui Jin, Sijin Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Tong He, Yong Yu, Zheng Zhang, Alex Smola

In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.


Regioned Episodic Reinforcement Learning

no code implementations1 Jan 2021 Jiarui Jin, Cong Chen, Ming Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Yong Yu, Jun Wang, Alex Smola

Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.


Non-iterative Parallel Text Generation via Glancing Transformer

no code implementations1 Jan 2021 Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei LI

Although non-autoregressive models with one-iteration generation achieves remarkable inference speed-up, they still falls behind their autoregressive counterparts inprediction accuracy.

Language Modelling Text Generation

Improving Knowledge Tracing via Pre-training Question Embeddings

1 code implementation9 Dec 2020 Yunfei Liu, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, Yong Yu

Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.

Knowledge Tracing

Towards Generalized Implementation of Wasserstein Distance in GANs

1 code implementation7 Dec 2020 Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, Yong Yu

Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models.

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

no code implementations1 Nov 2020 Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu

To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.

Click-Through Rate Prediction Learning-To-Rank +1

Efficient Projection-Free Algorithms for Saddle Point Problems

no code implementations NeurIPS 2020 Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu

The Frank-Wolfe algorithm is a classic method for constrained optimization problems.

Model-based Policy Optimization with Unsupervised Model Adaptation

1 code implementation NeurIPS 2020 Jian Shen, Han Zhao, Weinan Zhang, Yong Yu

However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance.

Continuous Control Model-based Reinforcement Learning +1

AI Chiller: An Open IoT Cloud Based Machine Learning Framework for the Energy Saving of Building HVAC System via Big Data Analytics on the Fusion of BMS and Environmental Data

no code implementations9 Oct 2020 Yong Yu

Although many research works and projects turn to this direction for energy saving, the application into the optimization problem remains a challenging task.

GIKT: A Graph-based Interaction Model for Knowledge Tracing

2 code implementations13 Sep 2020 Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu, Wei-Nan Zhang, Yong Yu

With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.

Knowledge Tracing

Bidirectional Model-based Policy Optimization

1 code implementation ICML 2020 Hang Lai, Jian Shen, Wei-Nan Zhang, Yong Yu

Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate.

Decision Making Model-based Reinforcement Learning

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

1 code implementation1 Jul 2020 Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Wei-Nan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola

To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.

Recommendation Systems

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

no code implementations18 Jun 2020 Sijin Zhou, Xinyi Dai, Haokun Chen, Wei-Nan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu

Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.

Decision Making Recommendation Systems +1

User Behavior Retrieval for Click-Through Rate Prediction

1 code implementation28 May 2020 Jiarui Qin, Wei-Nan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, Yong Yu

These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones.

Click-Through Rate Prediction

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 Survival Analysis

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

1 code implementation3 Apr 2020 Yuxuan Song, Minkai Xu, Lantao Yu, Hao Zhou, Shuo Shao, Yong Yu

In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme.

Representation Learning

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

4 code implementations25 Mar 2020 Bin Liu, Chenxu Zhu, Guilin Li, Wei-Nan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.

Click-Through Rate Prediction Recommendation Systems

Multi-Agent Interactions Modeling with Correlated Policies

1 code implementation ICLR 2020 Minghuan Liu, Ming Zhou, Wei-Nan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu

In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.

Imitation Learning

Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

no code implementations21 Nov 2019 Yuxuan Song, Lantao Yu, Zhangjie Cao, Zhiming Zhou, Jian Shen, Shuo Shao, Wei-Nan Zhang, Yong Yu

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.

Unsupervised Domain Adaptation

Exploring Diverse Expressions for Paraphrase Generation

no code implementations IJCNLP 2019 Lihua Qian, Lin Qiu, Wei-Nan Zhang, Xin Jiang, Yong Yu

Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification.

Information Retrieval Paraphrase Generation +2

Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning

no code implementations10 Sep 2019 Liheng Chen, Hongyi Guo, Yali Du, Fei Fang, Haifeng Zhang, Yaoming Zhu, Ming Zhou, Wei-Nan Zhang, Qing Wang, Yong Yu

Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.

Multi-agent Reinforcement Learning reinforcement-learning

Towards Making the Most of BERT in Neural Machine Translation

1 code implementation15 Aug 2019 Jiacheng Yang, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Yong Yu, Wei-Nan Zhang, Lei LI

GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various natural language processing tasks.

Machine Translation Translation

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

1 code implementation KDD '19 2019 Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang

Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e. g., points of interests and road networks.

Graph Attention Meta-Learning +3

Dynamically Fused Graph Network for Multi-hop Reasoning

1 code implementation ACL 2019 Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei LI, Wei-Nan Zhang, Yong Yu

However, many difficult questions require multiple supporting evidence from scattered text among two or more documents.

Question Answering

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

1 code implementation13 May 2019 Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Wei-Nan Zhang, Yong Yu, Haiming Jin, Zhenhui Li

The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.

Multi-agent Reinforcement Learning reinforcement-learning

Deep Landscape Forecasting for Real-time Bidding Advertising

2 code implementations7 May 2019 Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Wei-Nan Zhang, Yong Yu

The problem is formulated as to forecast the probability distribution of market price for each ad auction.

Survival Analysis

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.

Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs

1 code implementation2 Apr 2019 Zhiming Zhou, Jian Shen, Yuxuan Song, Wei-Nan Zhang, Yong Yu

Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).

Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space

no code implementations4 Mar 2019 Zhou Fan, Rui Su, Wei-Nan Zhang, Yong Yu

In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks.


Lipschitz Generative Adversarial Nets

1 code implementation15 Feb 2019 Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Wei-Nan Zhang, Yong Yu, Zhihua Zhang

By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem.


Guiding the One-to-one Mapping in CycleGAN via Optimal Transport

no code implementations15 Nov 2018 Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu

CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation.


Large-scale Interactive Recommendation with Tree-structured Policy Gradient

no code implementations14 Nov 2018 Haokun Chen, Xinyi Dai, Han Cai, Wei-Nan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance.

Recommendation Systems

Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation

no code implementations14 Nov 2018 Haifeng Zhang, Zilong Guo, Han Cai, Chris Wang, Wei-Nan Zhang, Yong Yu, Wenxin Li, Jun Wang

With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.

HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

no code implementations28 Sep 2018 Zheyi Pan, Yuxuan Liang, Junbo Zhang, Xiuwen Yi, Yong Yu, Yu Zheng

In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models.

Spatio-Temporal Forecasting Time Series

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

no code implementations12 Sep 2018 Liheng Chen, Yanru Qu, Zhenghui Wang, Lin Qiu, Wei-Nan Zhang, Ken Chen, Shaodian Zhang, Yong Yu

TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance.

Graph Embedding Link Prediction

Deep Recurrent Survival Analysis

1 code implementation7 Sep 2018 Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Wei-Nan Zhang, Lin Qiu, Yong Yu

By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.

Survival Analysis

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

1 code implementation11 Aug 2018 Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang

To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.

Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets

1 code implementation2 Jul 2018 Zhiming Zhou, Yuxuan Song, Lantao Yu, Hongwei Wang, Jiadong Liang, Wei-Nan Zhang, Zhihua Zhang, Yong Yu

In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs.

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

7 code implementations1 Jul 2018 Yanru Qu, Bohui Fang, Wei-Nan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

Click-Through Rate Prediction Feature Engineering +2

Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

no code implementations NAACL 2018 Zhenghui Wang, Yanru Qu, Li-Heng Chen, Jian Shen, Wei-Nan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu

We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.

Medical Named Entity Recognition NER +1

Neural Text Generation: Past, Present and Beyond

no code implementations15 Mar 2018 Sidi Lu, Yaoming Zhu, Wei-Nan Zhang, Jun Wang, Yong Yu

This paper presents a systematic survey on recent development of neural text generation models.

reinforcement-learning Text Generation

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

no code implementations1 Mar 2018 Kan Ren, Wei-Nan Zhang, Ke Chang, Yifei Rong, Yong Yu, Jun Wang

From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes.

Unsupervised Deep Domain Adaptation for Pedestrian Detection

no code implementations9 Feb 2018 Lihang Liu, Weiyao Lin, Lisheng Wu, Yong Yu, Michael Ying Yang

This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes.

Pedestrian Detection Unsupervised Domain Adaptation

Texygen: A Benchmarking Platform for Text Generation Models

1 code implementation6 Feb 2018 Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Wei-Nan Zhang, Jun Wang, Yong Yu

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.

Text Generation

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

3 code implementations2 Dec 2017 Lianmin Zheng, Jiacheng Yang, Han Cai, Wei-Nan Zhang, Jun Wang, Yong Yu

Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.

Multi-agent Reinforcement Learning reinforcement-learning

Supervised Hashing based on Energy Minimization

no code implementations2 Dec 2017 Zihao Hu, Xiyi Luo, Hongtao Lu, Yong Yu

Recently, supervised hashing methods have attracted much attention since they can optimize retrieval speed and storage cost while preserving semantic information.

Face Transfer with Generative Adversarial Network

no code implementations17 Oct 2017 Runze Xu, Zhiming Zhou, Wei-Nan Zhang, Yong Yu

Face transfer animates the facial performances of the character in the target video by a source actor.

Face Transfer

Long Text Generation via Adversarial Training with Leaked Information

6 code implementations24 Sep 2017 Jiaxian Guo, Sidi Lu, Han Cai, Wei-Nan Zhang, Yong Yu, Jun Wang

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.

Text Generation

A Study of AI Population Dynamics with Million-agent Reinforcement Learning

no code implementations13 Sep 2017 Yaodong Yang, Lantao Yu, Yiwei Bai, Jun Wang, Wei-Nan Zhang, Ying Wen, Yong Yu

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.


Efficient Architecture Search by Network Transformation

3 code implementations16 Jul 2017 Han Cai, Tianyao Chen, Wei-Nan Zhang, Yong Yu, Jun Wang

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results.

Image Classification Neural Architecture Search +1

Wasserstein Distance Guided Representation Learning for Domain Adaptation

8 code implementations5 Jul 2017 Jian Shen, Yanru Qu, Wei-Nan Zhang, Yong Yu

Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).

Domain Adaptation General Classification +2

Learning to Design Games: Strategic Environments in Reinforcement Learning

no code implementations5 Jul 2017 Haifeng Zhang, Jun Wang, Zhiming Zhou, Wei-Nan Zhang, Ying Wen, Yong Yu, Wenxin Li

In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.


Real-Time Bidding by Reinforcement Learning in Display Advertising

1 code implementation10 Jan 2017 Han Cai, Kan Ren, Wei-Nan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, Defeng Guo

In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.


Product-based Neural Networks for User Response Prediction

10 code implementations1 Nov 2016 Yanru Qu, Han Cai, Kan Ren, Wei-Nan Zhang, Yong Yu, Ying Wen, Jun Wang

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.

Click-Through Rate Prediction Recommendation Systems

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

22 code implementations18 Sep 2016 Lantao Yu, Wei-Nan Zhang, Jun Wang, Yong Yu

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data.

Text Generation

A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia

no code implementations16 Oct 2015 Chenhao Zhu, Kan Ren, Xuan Liu, Haofen Wang, Yiding Tian, Yong Yu

We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB).

Question Answering

A Latent Clothing Attribute Approach for Human Pose Estimation

no code implementations16 Nov 2014 Weipeng Zhang, Jie Shen, Guangcan Liu, Yong Yu

Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes.

Action Recognition Pose Estimation

A Parallel and Efficient Algorithm for Learning to Match

no code implementations22 Oct 2014 Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu

In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization.

Collaborative Filtering Link Prediction

Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification

no code implementations19 Apr 2014 Jie Shen, Guangcan Liu, Jia Chen, Yuqiang Fang, Jianbin Xie, Yong Yu, Shuicheng Yan

In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications.

General Classification Pose Estimation

Feature-Based Matrix Factorization

no code implementations11 Sep 2011 Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, Yong Yu

Recommender system has been more and more popular and widely used in many applications recently.

Recommendation Systems

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