Tao Qin

About

Dr. Tao Qin (秦涛) is a Partner Research Manager at Microsoft Research AI for Science. His research interests include deep learning (with applications to machine translation, healthcare, speech synthesis and recognition, music understanding and composition), reinforcement learning (with applications to games and real-world problems), game theory and multi-agent systems (with applications to cloud computing, online and mobile advertising), and information retrieval and computational advertising. Most recently, he focuses on AI for science, especially molecular modeling and design, drug discovery and design, biochemistry, etc. He got his PhD degree and Bachelor degree both from Tsinghua University. He is a senior member of ACM and IEEE, and an Adjunct Professor (PhD advisor) in the University of Science and Technology of China.

His team helped Microsoft achieve human parity in Chinese-English machine translation in 2018, won the first place for 8 translation tasks in WMT 2019, and built the world-best Mahjong AI, named Suphx, which achieved 10 DAN on the Tenhou platform in mid 2019.

We are hiring!
We are hiring all levels of researchers! Please email me (taoqin AT microsoft DOT com) if you have good coding skills and are passionate about machine learning research for natural science problems.

Recent updates
Focus on AI for science
Action Editor for Transactions on Machine Learning Research (TMLR)
BioGPT: a GPT model for biomedical domain [paper][code/model]
Research shows MPNet generates the most effective sentence embeddings among approximately 40 pretrained models tested.
Our paper “TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets” has been selected as the ICDM 2022 Best Student Paper Award runner-up.

AI4Science
Accelerating protein engineering with fitnesslandscape modeling and reinforcement learning. bioRxiv 2023.
Microsoft Research AI4Science, Microsoft Azure Quantum. The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4. Arxiv 2023.
Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan, FABind: Fast and Accurate Protein-Ligand Binding. NeurIPS 2023.
Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu. SMT-DTA: Improving Drug-Target Affinity Prediction with Semi-supervised Multi-task Training. Briefings in Bioinformatics 2023.
Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu. Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design. KDD 2023.
Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu. Dual-view Molecular Pre-training. KDD 2023.
Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu. Retrosynthetic Planning with Dual Value Networks. ICML 2023.
Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu. De Novo Molecular Generation via Connection-aware Motif Mining. ICLR 2023.
Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie, Qi Meng, Lijun Wu, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu. O-GNN: incorporating ring priors into molecular modeling. ICLR 2023.
Jiacheng Lin, Lijun Wu, Jinhua Zhu, Xiaobo Liang, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu. R2-DDI: Relation-aware Feature Refinement for Drug-Drug Interaction Prediction. Briefings in Bioinformatics 2022.
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu. Direct Molecular Conformation Generation. TMLR 2022.
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, Tie-Yan Liu. BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining. Briefings in Bioinformatics. [code]
Lijun Wu, Chengcan Yin, Jinhua Zhu, Zhen Wu, Liang He, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu. SPRoBERTa: Protein Embedding Learning with Local Fragment Modeling. Briefings in Bioinformatics.
Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu. Unified 2D and 3D Pre-Training of Molecular Representations. KDD 2022.
Shufang Xie, Peng Han, Yingce Xia, Lijun Wu, Tao Qin, Chenjuan Guo, Bin Yang, Rui Yan. RetroGraph: Retrosynthetic Planning with Graph Search. KDD 2022.
Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Tao Qin, Tie-Yan Liu, Discovering Drug-Target Interaction Knowledge from Biomedical Literature. Bioinformatics, 2022. [code]
Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu. Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model. arXiv 2021.
Yang Fan, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Tao Qin. Back Translation for Molecule Generation. Bioinformatics, 2021. [code]

Featured work

Suphx: The World Best Mahjong AI

We built Suphx, the first 10 DAN AI for Mahjong,  based on deep reinforcement learning with some newly introduced techniques including global reward prediction, oracle guiding, and run-time policy adaptation. [news][paper]

• Review by human players (most in Japanese)
• A book about Suphx written by a topmost Mahjong professional

FastSpeech: Fast and Robust Speech Synthesis

• We designed FastSpeech, a novel feed-forward network based on Transformer, to generate mel-spectrograms in parallel for TTS.
• Compared with autoregressive Transformer TTS, FastSpeech speeds up mel-spectrogram generation by 270x and the end-to-end speech synthesis by 38x on V100.
• Our FastSpeech and FastSpeech 2 support all the neural TTS models in Azure, with 70+ languages/locals and 200+ voices!  [News-1] [News-2]

Neural Machine Translation

• We achieved human parity in translating news from Chinese to English in 2018.
• My team achieved eight top places in the recent machine translation challenge organized by the fourth Conference on Machine Translation (WMT19).
• Our dual learning has been integrated into Microsoft Translator to enhance the translation of multiple languages, including Chinese, German, French, Hindi, Italian, Spanish, Japanese, Korean, and Russian, from and to English. [news]
• Our MASS algorithm [code], the first pre-training model for sequence to sequence generation, enables the translation of 10+ rare languages. [news]

Dual Learning

Dual learning is a learning framework that leverages structural duality between AI tasks. This book gives a comprehensive review of recent research on dual learning. We introduce its basic principles, including dual reconstruction, joint-probability equation, and marginal probability equation, and cover various learning settings and algorithms, including dual semi-supervised learning, dual unsupervised learning, dual supervised learning, and dual inference. For each setting, we introduce diverse applications, such as machine translation, image-to-image translation, speech synthesis and recognition, question answering and generation, image classification and generation, code summarization and generation, sentiment analysis, etc. This book is written for researchers and graduate/undergraduate students

AI for Music

We are developing deep learning algorithms for music understanding and generation.

• For music understanding, we work on symbolic music understanding (MusicBERT) and automatic lyrics transcription (PDAugment).
• For music generation, we work on songwriting (SongMASS), lyric generation (DeepRapper), melody generation (TeleMelody), accompaniment generation (PopMAG), and singing voice synthesis (HiFiSinger).

Our code and generated samples.

Talks and Tutorials
Tutorial on recent advances in neural speech synthesis at ICASSP 2022
Tutorial on neural speech synthesis at IJCAI 2021
Tutorial on dual learning at IJCAI 2019
Tutorial on dual learning at ACML 2018
Keynote on neural machine translation at ACML 2018 Workshop on Multi-output Learning
Efficient neural machine translation at GTC China 2018

Open Source & Datasets
R-Drop: Regularized Dropout for Neural Networks, NeurIPS 2021. [Code@GitHub] R-Drop is a simple yet powerful variant of dropout, by minimizing the bidirectional KL-divergence of the output distributions of any pair of sub models sampled from dropout in model training, and makes significant improvements on 5 widely used deep learning tasks including neural machine translation, abstractive summarization, language understanding, language modeling, and image classification, with 18 datasets in total.
Fully Parameterized Quantile Function for Distributional Reinforcement Learning, NeurIPS 2019. [Code@GitHub]
Incorporating BERT into Neural Machine Translation, ICLR 2020. [Code@GitHub]
MPNet: Masked and Permuted Pre-training for Language Understanding [Code@GitHub]
MASS: Masked Sequence to Sequence Pre-training for Language Generation [Code@GitHub]
Efficient Training of BERT by Progressively Stacking [Code@GitHub]
Automatic Neural Architecture Optimization in a continuous and differentiable space [paper][Code@GitHub]
Improve word embeddings with adversarial training [paper][Code@Github]
Supervised Dual Learning for image classification/generation and sentiment analysis, [Code@Github]
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks, [Code@GitHub]
Microsoft Learning to Rank Datasets with tens of thousands of queries and millions of documents have been released. If you find any problems or have any suggestions, please let us know.
LETOR: the first public learning to rank data collection. Reference paper & Bibtex

Blogs and articles about our work
AutoML: Discovering the best neural architectures in the continuous space
Learning to teach: Mutually enhanced learning and teaching for artificial intelligence
训练可解释、可压缩、高准确率的LSTM [Chinese article]
We matched human performance in translating news from Chinese to English
Learning to Teach：让AI和机器学习算法教学相长  [Chinese article]
Deliberation Network: Pushing the frontiers of neural machine translation
深度学习的下一步如何发展？@知乎
LightRNN：深度学习之以小见大
对偶学习

Book
Tao Qin. Dual Learning. Springer 2020.

Foundation Models
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, Tie-Yan Liu. BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining. Briefings in Bioinformatics. [code]
Lijun Wu, Chengcan Yin, Jinhua Zhu, Zhen Wu, Liang He, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu. SPRoBERTa: Protein Embedding Learning with Local Fragment Modeling. Briefings in Bioinformatics.
Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, Dongsheng Li. Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling. NeurIPS 2022.
Jin Xu, Xu Tan, Renqian Luo, Kaitao Song, Jian Li, Tao Qin, and Tie-Yan Liu. NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search. KDD 2021.
Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, and Tie-Yan Liu. MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training. ACL 2021.
Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, Tao Qin. SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint. AAAI 2021.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for Language Understanding. NeurIPS 2020.
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, Incorporating BERT into Neural Machine Translation, ICLR 2020. [code]
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation, ICML 2019. [code]
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, and Tie-Yan Liu, Efficient Training of BERT by Progressively Stacking, ICML 2019. [code]

Surveys
Xu Tan, Tao Qin, Frank Soong, and Tie-Yan Liu. A Survey on Neural Speech Synthesis. Arxiv 2021.
Rui Wang, Xu Tan, Renqian Luo, Tao Qin and Tie-Yan Liu. A Survey on Low-Resource Neural Machine Translation. IJCAI 2021.
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, and Tao Qin. Generalizing to Unseen Domains: A Survey on Domain Generalization. IJCAI 2021.

Previous Papers
Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, Houqiang Li. Making Better Decisions by Directly Planning in Continuous Control. ICLR 2023.
Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, Tieyan Liu. TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets. ICDM 2022. Best Student Paper Runner-Up
Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu. Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. NeurIPS 2022.
Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu. An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context. NeurIPS 2022.
Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, Tie-Yan Liu. Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation. NeurIPS 2022.
Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, Dongsheng Li. Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling. NeurIPS 2022.
Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu, Jiawei Chen, Xu Tan, Danilo Mandic, Lei He, Xiangyang Li, Tao Qin, sheng zhao, Tie-Yan Liu. BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis. NeurIPS 2022.
Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li. MAGIC-NAS: Analyzing and Mitigating Interference in Neural Architecture Search. ICML 2022.
Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu. Supervised Off-Policy Ranking. ICML 2022.
Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff, Tie-Yan Liu. Pixel-based Automated Game Testing via Exploration, Detection, and Investigation. CoG 2022.
Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, Houqiang Li. Masked Contrastive Representation Learning for Reinforcement Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022.
Akiko Eriguchi, Shufang Xie, Hany Hassan, Tao Qin. Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations. NAACL 2022.
Kexun Zhang, Rui Wang, Xu Tan, Junliang Guo, Yi Ren, Tao Qin, Tie-Yan Liu. A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation. NAACL 2022.
Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu. PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior. ICLR 2022.
Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama. Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022.
Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, Tao Qin. GNN is a Counter? Revisiting GNN for Question Answering. ICLR 2022.
Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Rui Yan, Tie-Yan Liu. Target-Side Data Augmentation for Sequence Generation. ICLR 2022.
Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu. Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. ICLR 2022.
Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu. Recovering Latent Causal Factor for Generalization to Distributional Shifts. NeurIPS 2021.
Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu. Learning Causal Semantic Representation for Out-of-Distribution Prediction. NeurIPS 2021.
Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu. Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. NeurIPS 2021.
Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu. Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning. NeurIPS 2021.
Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu. On the Generative Utility of Cyclic Conditionals. NeurIPS 2021.
Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu. R-Drop: Regularized Dropout for Neural Networks. NeurIPS 2021.
Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen, Tao Qin, Tie-Yan Liu. Speech-T: Transducer for Text to Speech and Beyond. NeurIPS 2021.
Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiangyang Li, Edward Lin, Tie-Yan Liu. FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition. NeurIPS 2021.
Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, and Dongsheng Li. Towards Generating Real-World Time Series Data. ICDM 2021.
Zhibing Zhao, Yingce Xia, Tao Qin, Lirong Xia, and Tie-Yan Liu. Dual Learning: Theoretical Study and an Algorithmic Extension. SN Computer Science.
Jin Xu, Xu Tan, Renqian Luo, Kaitao Song, Jian Li, Tao Qin, and Tie-Yan Liu. NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search. KDD 2021.
Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, and Tie-Yan Liu. Temporally Correlated Task Scheduling for Sequence Learning. ICML 2021.
Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, and Tie-Yan Liu. DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling. ACL 2021.
Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, and Tie-Yan Liu. MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training. ACL 2021.
Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, and Tie-Yan Liu. Independence-aware Advantage Estimation. IJCAI 2021.
Jianxin Lin, Zhibo Chen, Yingce Xia, Sen Liu, Tao Qin, Jiebo Luo. Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation. TPAMI 2021.
Yuzi Yan, Xu Tan, Bohan Li, Tao Qin, Sheng Zhao, Yuan Shen, Tie-Yan Liu. AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data. ICASSP 2021.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Jinzhu Li, Sheng Zhao, Enhong Chen, Tie-Yan Liu. LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search. ICASSP 2021.
Chen Zhang, Yi Ren, Xu Tan, Jinglin Liu, Kejun Zhang, Tao Qin, Sheng Zhao, Tie-Yan Liu. DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling. ICASSP 2021.
Linghui Meng, Jin Xu, Xu Tan, Jindong Wang, Tao Qin, Bo Xu. MixSpeech: Data Augmentation for Low-Resource Automatic Speech Recognition. ICASSP 2021.
Yichong Leng, Xu Tan, Sheng Zhao, Frank Soong, Xiang-Yang Li, Tao Qin. MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network. ICASSP 2021.
Zhihan Zhang, Xiubo Geng, Tao Qin, Yunfang Wu, Daxin Jiang. Knowledge-Aware Procedual Text Understanding with Multi-Stage Training. WWW 2021.
Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, Sheng zhao, Tie-Yan Liu. AdaSpeech: Adaptive Text to Speech for Custom Voice. ICLR 2021.
Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Li Jian, Nenghai Yu, Tie-Yan Liu. Return-Based Contrastive Representation Learning for Reinforcement Learning. ICLR 2021.
Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu. FastSpeech 2: Fast and High-Quality End-to-End Text to Speech. ICLR 2021.
Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu. IOT: Instance-wise Layer Reordering for Transformer Structures. ICLR 2021.
Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Xiangyang Li, Tao Qin. Learning to Reweight with Deep Interactions. AAAI 2021.
Chen Zhang, Xu Tan, Yi Ren, Tao Qin, Kejun Zhang, Tie-Yan Liu. UWSpeech: Speech to Speech Translation for Unwritten Languages. AAAI 2021.
Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, Tao Qin. SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint. AAAI 2021.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu. Semi-Supervised Neural Architecture Search. NeurIPS 2020.
Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu. RD^2: Reward Decomposition with Representation Decomposition. NeurIPS 2020.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for Language Understanding. NeurIPS 2020.
Mucheng Ren, Xiubo Geng, Tao QIN, Heyan Huang and Daxin Jiang. Towards Interpretable Reasoning over Paragraph Effects in Situation. EMNLP 2020.
Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie-Yan Liu. PopMAG: Pop Music Accompaniment Generation. Multimedia 2020.
Weicong Chen, Xu Tan, Yingce Xia, Tao Qin, Yu Wang, and Tie-Yan Liu. DualLip: A System for Joint Lip Reading and Generation. Multimedia 2020.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural Architecture Search with GBDT. arXiv 2020.
Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu, FastSpeech 2: Fast and High-Quality End-to-End Text-to-Speech. arXiv 2020.
Mingjian Chen, Xu Tan, Yi Ren, Jin Xu, Hao Sun, Sheng Zhao, Tao Qin, and Tie-Yan Liu, MultiSpeech: Multi-Speaker Text to Speech with Transformer. INTERSPEECH 2020.
Jia Xing, Shuxin Zheng, Dian Ding, James T. Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey Jang, Yun Zh, Haotian Zheng, Lu Ren, Tie-Yan Liu, Jiming Hao. Deep learning for prediction of the air quality response to emission changes. Environmental Science & Technology 2020.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural Architecture Search with GBDT. arXiv 2020.
Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, and Hsiao-Wuen Hon. Suphx: Mastering Mahjong with Deep Reinforcement Learning. arXiv 2020.
Jin Xu, Xu Tan, Yi Ren, Tao Qin, Jian Li, Sheng Zhao, and Tie-Yan Liu. LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition. KDD 2020.
Yi Ren, Xu Tan, Tao Qin, Jian Luan, Zhou Zhao, and Tie-Yan Liu. DeepSinger: Singing Voice Synthesis with Data Mined From the Web. KDD 2020.
Yang Fan, Fei Tian, Yingce Xia, Tao Qin, Xiangyang Li, and Tie-Yan Liu. Searching Better Architectures for Neural Machine Translation. IEEE/ACM Transactions on Audio, Speech and Language Processing 2020.
Yi Ren, Jinglin Liu, Xu Tan, Chen Zhang, Tao QIN, Zhou Zhao, and Tie-Yan Liu. SimulSpeech: End-to-End Simultaneous Speech to Text Translation. ACL 2020.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu, Semi-Supervised Neural Architecture Search, arxiv 2020. [code]
Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. Sequence Generation with Mixed Representations, ICML 2020.
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, Incorporating BERT into Neural Machine Translation, ICLR 2020. [code]
Yiren Wang, Lijun Wu, Yingce Xia, Tao Qin, Cheng Xiang Zhai, Tie-Yan Liu, Transductive Ensemble Learning for Neural Machine Translation, AAAI 2020.
Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Tie-Yan Liu, Enhong Chen, Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation, AAAI 2020.
Yiping Lu, Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, and Tie-Yan Liu, Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View, arxiv 2019.
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu, FastSpeech: Fast, Robust and Controllable Text to Speech, NeurIPS 2019.
Derek Yang, Li Zhao, Zichuan Lin, Jiang Bian, Tao Qin, and Tie-Yan Liu, Fully Parameterized Quantile Function for Distributional Reinforcement Learning, NeurIPS 2019. [code]
Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Guangwen Yang, and Tie-Yan Liu, Distributional Reward Decomposition for Reinforcement Learning, NeurIPS 2019.
Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Neural Machine Translation with Soft Prototype, NeurIPS 2019. [code]
Lu Hou, Jinhua Zhu, James Tin-Yau Kwok, Fei Gao, Tao Qin, and Tie-Yan Liu, Normalization Helps Training of Quantized LSTM, NeurIPS 2019. [code]
Hao Sun, Xu Tan, Jun-Wei Gan, Sheng Zhao, Dongxu Han, Hongzhi Liu, Tao Qin, and Tie-Yan Liu, Knowledge Distillation from BERT in Pre-training and Fine-tuning for Polyphone Disambiguation, ASRU 2019.
Hao Sun, Xu Tan, Jun-Wei Gan, Hongzhi Liu, Sheng Zhao, Tao Qin, and Tie-Yan Liu, Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion, InterSpeech 2019.
Lijun Wu, Xu Tan, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019.
Jianxin Lin, Zhibo Chen, Yingce Xia, Sen Liu, Tao Qin, and Jiebo Luo. Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Enhong Chen, and Tie-Yan Liu, Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019.
Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao QIN, Liwei WANG, and Tie-Yan Liu, Hint-based Training for Non-AutoRegressive Machine Translation. EMNLP 2019.
Lijun Wu, Jinhua Zhu, Fei Gao, Di He, Tao QIN, Jianhuang Lai, and Tie-Yan Liu, Machine Translation With Weakly Paired Documents. EMNLP 2019.
Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao QIN, and Tie-Yan Liu, Multilingual Neural Machine Translation with Language Clustering. EMNLP 2019.
Lijun Wu, Yiren Wang, Yingce Xia, Tao Qin, Jianwen Lai, and Tie-Yan Liu, Exploiting Monolingual Data at Scale for Neural Machine Translation. EMNLP 2019.
Tao Shen, Xiubo Geng, Tao QIN, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, and Daxin Jiang, Multi-task Learning for Conversational Question Answering Over a Large-Scale Knowledge Base. EMNLP 2019.
Yingce Xia, Xu Tan, et al. Microsoft Research Asia’s Systems for WMT19, the fourth Conference on Machine Translation.
Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xueqi Cheng, and Tie-Yan Liu, Soft Contextual Data Augmentation for Neural Machine Translation, ACL 2019.
Yichong Leng, Xu Tan, Tao QIN, Xiang-Yang Li and Tie-Yan Liu, Unsupervised Pivot Translation for Distant Languages, ACL 2019.
Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao QIN and Tie-Yan Liu, Depth Growing for Neural Machine Translation, ACL 2019.,
Jianxin Lin, Yingce Xia, Tao Qin, Yijun Wang, Zhibo Chen, Image-to-Image Translation with Multi-Path Consistency Regularization, IJCAI 2019.
Tianyu He, Yingce Xia, Jianxin Lin, Xu Tan, Di He, Tao Qin, Zhibo Chen, Deliberation Learning for Image-to-Image Translation, IJCAI 2019.
Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu, Polygon-Net: A General Framework for Jointly Boosting Multiple Unsupervised Neural Machine Translation Models, IJCAI 2019.
Zhige Li, Derek Yang, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu, Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding, KDD 2019.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation, ICML 2019. [code]
Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Sheng Zhao, Tie-Yan Liu, Almost Unsupervised Text to Speech and Automatic Speech Recognition, ICML 2019.
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, and Tie-Yan Liu, Efficient Training of BERT by Progressively Stacking, ICML 2019. [code]
Jiang Rong, Tao Qin, Bo An, Competitive Bridge Bidding with Deep Neural Networks, AAMAS 2019.
Yibo Sun, Duyu Tang, Nan Duan, Tao Qin, Shujie Liu, Zhao Yan, Ming Zhou, Yuanhua Lv, Wenpeng Yin, Xiaocheng Feng, Bing Qin, Ting Liu, Joint Learning of Question Answering and Question Generation, TKDE 2019.
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu, Multi-Agent Dual Learning, ICLR 2019.
Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tieyan Liu，Representation Degeneration Problem in Training Natural Language Generation Models, ICLR 2019.
Xu Tan, Yi Ren, Di He, Tao Qin, Tie-Yan Liu, Multilingual Neural Machine Translation with Knowledge Distillation, ICLR 2019.
Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Trust Region Evolution Strategies, AAAI 2019.
Yiren Wang, Fei Tian, Di He, Tao Qin, Chengxiang Zhai, Tie-Yan Liu, Non-Autoregressive Machine Translation with Auxiliary Regularization, AAAI 2019.
Junliang Guo, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu, Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input, AAAI 2019.
Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin, Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder, AAAI 2019.
Chengyue Gong, Xu Tan, Di He, and Tao Qin, Sentence-wise Smooth Regularization for Sequence to Sequence Learning, AAAI 2019.
Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu, FRAGE: Frequency-Agnostic Word Representation, NIPS 2018. [code]
Lijun Wu, Fei Tian, Yingce Xia, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Learning to Teach with Dynamic Loss Functions, NIPS 2018.
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu, Neural Architecture Optimization, NIPS 2018. [code]
Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation, NIPS 2018.
Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai and Tie-Yan Liu, A Study of Reinforcement Learning for Neural Machine Translation, EMNLP 2018.
Xu Tan, Lijun Wu, Di He, Fei Tian, Tao QIN, Jianhuang Lai, and Tie-Yan Liu, Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter, EMNLP 2018.
Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning, ICML 2018.
Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Adversarial Neural Machine Translation, ACML 2018.
Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, and Tie-Yan Liu, Towards Binary-Valued Gates for Robust LSTM Training, ICML 2018. [code] [Chinese article]
Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, and Tie-Yan Liu, Double Path Networks for Sequence to Sequence Learning, COLING 2018.
Hany Hassan, et al. Achieving Human Parity on Automatic Chinese to English News Translation, arXiv 2018.
Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Conditional Image-to-Image Translation, CVPR 2018.
Fei Gao, Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Efficient Sequence Learning with Group Recurrent Networks, NAACL 2018.
Yanyao Shen, Xu Tan, Di He, Tao QIN, and Tie-Yan Liu, Dense Information Flow for Neural Machine Translation, NAACL 2018. [code]
Yang Fan, Fei Tian, Tao Qin, Xiangyang Li, and Tie-Yan Liu, Learning to Teach, ICLR 2018. [Chinese article]
Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, and Tie-Yan Liu, Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization, AAAI 2018.
Jiang Rong, Tao Qin, and Bo An, Dynamic Pricing for Reusable Resources in Competitive Market with Stochastic Demand, AAAI 2018.
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou, Question Answering and Question Generation as Dual Tasks, arXiv 2017.
Chang Xu, Tao Qin, Gang Wang, and Tie-Yan Liu, Reinforcement Learning for Learning Rate Control, arXiv 2017.
Aadharsh Kannan, Justin Rao, Preston McAfee, Di He, Tao Qin and Tie-Yan Liu, Scale Effects in Web Search, WINE 2017.
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, and Tie-Yan Liu, Deliberation Networks: Sequence Generation Beyond One-Pass Decoding, NIPS 2017.
Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, and Tie-Yan Liu, Decoding with Value Networks for Neural Machine Translation, NIPS 2017.
Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu and Tie-Yan Liu, Dual Supervised Learning,  ICML 2017.
Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Sequence Prediction with Unlabeled Data by Reward Function Learning, IJCAI 2017.
Yingce Xia, Jiang Bian, Tao Qin, Tie-Yan Liu, Dual Inference for Machine Learning, IJCAI 2017.
Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, Bo Zheng, Efficient Mechanism Design for Online Scheduling (Extended Abstract), IJCAI 2017.
Yingce Xia, Tao Qin, Wenkui Ding, Haifang Li, Xu-Dong Zhang, Nenghai Yu and Tie-Yan Liu, Finite Budget Analysis of Multi-armed Bandit Problems, Neurocomputing.
Chang Xu, Tao Qin, Yalong Bai, Gang Wang and Tie-Yan Liu, Convolutional Neural Networks for Posed and Spontaneous Expression Recognition, ICME 2017.
Jiang Rong, Tao Qin, Bo An and Tie-Yan Liu, Pricing Optimization for Selling Reusable Resources, AAMAS 2017.
Jiang Rong, Tao Qin, Bo An and Tie-Yan Liu, Revenue Maximization for Finitely Repeated Ad Auctions, AAAI 2017.
Jia Zhang, Weidong Ma, Tao Qin, Xiaoming Sun and Tie-Yan Liu, Randomized Mechanisms for Selling Reserved Instances in Cloud Computing, AAAI 2017.
Yingce Xia, Fei Tian, Tao Qin, Nenghai Yu and Tie-Yan Liu, Sequence Generation with Target Attention, ECML 2017.
Xiang Li, Tao Qin, Jian Yang, and Tie-Yan Liu, LightRNN: Memory and Computation-Efficient Recurrent Neural Networks, NIPS 2016. [Code@GitHub]
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma, Dual Learning for Machine Translation, NIPS 2016.
Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, Bo Zheng, Efficient Mechanism Design for Online Scheduling, accepted by Journal of Artificial Intelligence Research (JAIR), 2016.
Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu, Modeling Bounded Rationality for Sponsored Search Auctions, ECAI 2016.
Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu, Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays, IJCAI 2016. [full version]
Yingce Xia, Tao Qin, Nenghai Yu, Tie-Yan Liu, Best Action Selection in a Stochastic Environment, AAMAS 2016.
Tie-Yan Liu, Weidong Ma, Pingzhong Tang, Tao Qin, Guang Yang, Bo Zheng, Online Non-Preemptive Story Scheduling in Web Advertising, AAMAS 2016.
Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu, Optimal Sample Size for Adword Auctions, AAMAS 2016, short paper.
Bo Zheng, Li Xiao, Guang Yang, Tao Qin, Online Posted-Price Mechanism with a Finite Time Horizon, AAMAS 2016, short paper.
Qizhen Zhang, Haoran Wang, Yang Chen, Tao Qin, Ying Yan, Thomas Moscibroda, A Shapley Value Approach for Cost Allocation in the Cloud, SOCC 2015, poster.
Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Thompson Sampling for Budgeted Multi-armed Bandits, IJCAI 2015.
Bolei Xu, Tao Qin, Guoping Qiu, and Tie-Yan Liu, Optimal Pricing for the Competitive and Evolutionary Cloud Market, IJCAI 2015.
Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Selling Reserved Instances in Cloud Computing, IJCAI 2015.
Long Tran-Thanh, Yingce Xia, Tao Qin, Nick Jenning, Efficient Algorithms with Performance Guarantees for the Stochastic Multiple-Choice Knapsack Problem, IJCAI 2015.
Bnyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals, AAMAS 2015.
Changjun Wang, Weidong Ma, Tao Qin, Feidiao Yang, Xujin Chen, Xiaodong Hu, and Tie-Yan Liu, New Mechanisms for Selling Reserved Instances in Cloud Computing, AAMAS 2015, short paper.
Bolei Xu, Tao Qin, Guoping Qiu, Tie-Yan Liu, Competitive Pricing for Cloud Computing in Evolutionary Market, AAMAS 2015, short paper.
Jiang Rong, Tao Qin, and Bo An. Quantal Response Equilibrium for Sponsored Search Auctions, AAMAS 2015, short paper.
Hafang Li, Fei Tian, Wei Chen, Tao Qin, Zhi-Ming Ma, and Tie-Yan Liu, Generalization Analysis for Game-Theoretic Machine Learning, AAAI 2015.
Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, AAAI 2015.
Junpei Komiyama and Tao Qin, Time-Decaying Bandits for Non-stationary Systems, WINE 2014.
Tao Qin, Wei Chen, and Tie-Yan Liu. Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology, 2014.
Jiang Rong, Tao Qin, and Bo An. Quantal Response Equilibrium for Sponsored Search Auctions: Computation and Inference, Ad Auctions 2014, in conjunction with EC 2014.
Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, and Liwei Wang. Generalized Second Price Auction with Probabilistic Broad Match, EC 2014.
Yingce Xia, Tao Qin, and Tie-Yan Liu. Incentivizing High-quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium, AAAI 2014.
Fei Tian, Haifang Li, Wei Chen, Tao Qin, and Tie-Yan Liu. Agent Behavior Prediction and Its Generalization Analysis, AAAI 2014.
Weidong Ma, Tao Qin, and Tie-Yan Liu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014. [poster]
Weihao Kong, Jian Li, Tao Qin, and Tie-Yan Liu, Optimal Allocation for Chunked-Reward Advertising, WINE 2013.
Wenkui Ding, Tao Wu, Tao Qin, and Tie-Yan Liu, Price of Anarchy for Generalized Second Price Auction,  arXiv:1305.5404.
Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
Wenkui Ding, Tao Qin, Xu-Dong Zhang and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
Xiubo Geng, Tao Qin, Xue-Qi Cheng and Tie-Yan Liu, A Noise-Tolerant Graphical Model for Ranking, Information Processing and Management, 2012.
Sungchul Kim, Tao Qin, Hwanjo Yu and Tie-Yan Liu, An Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search, CIKM 2011.
Xiubo Geng, Tie-Yan Liu, Tao Qin, Xue-Qi Cheng and Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
Tao Qin, Xiu-Bo Geng and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
Wenkui Ding, Tao Qin and Xu-Dong Zhang, Learning to Rank with Supplementary Data, AIRS 2010.
Yajuan Duan, Long Jiang, Tao Qin, Ming Zhou and Harry Shum. An Empirical Study on Learning to Rank of Tweets, COLING 2010.
Jiang Bian, Tie-Yan Liu, Tao Qin, Hongyuan Zha. Ranking with Query-Dependent Loss for Web Search, WSDM 2010.
Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval Journal, 2010.
Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li, LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval, Information Retrieval Journal, 2010.
Zhengya Sun, Tao Qin, Jue Wang, Qing Tao. Robust Sparse Rank Learning for Non-Smooth Ranking Measures, SIGIR 2009.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008. [Oral Paper]
Yan-Yan Lan, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, Hang Li. Query-Level Stability and Generalization in Learning to Rank, ICML 2008.
Xiu-Bo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, Heung-Yeung Shum. Query Dependent Ranking Using K-Nearest Neighbor, SIGIR 2008.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wenying Xiong, Hang Li. Learning to Rank Relational Objects and Its Application to Web Search, WWW 2008.
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. Query-level Loss Functions for Information Retrieval. Information Processing and Management, 2008. [DOI]
Tie-Yan Liu, Jun Xu, Tao Qin, Wenying Xiong, Hang Li. LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval, SIGIR 2007 workshop: Learning to Rank for Information Retrieval.
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach, ICML 2007.
Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
Xiubo Geng, Tie-Yan Liu, Tao Qin, Hang Li. Feature Selection for Ranking, SIGIR 2007.
Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
Yu-Ting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, Hang Li. Supervised Rank Aggregation, WWW 2007.
Bin Gao, Tie-Yan Liu, Tao Qin, Xin Zheng, Qian-Sheng Cheng, Wei-Ying Ma. Web Image Clustering by Consistent Utilization of Low-level Features and Surrounding Texts, ACM Multimedia 2005.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, Wei-Ying Ma. A Study of Relevance Propagation for Web Search, SIGIR 2005.