Learn from microbial intelligence for avermectins overproduction.
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-257. doi: 10.1016/j.copbio.. Epub
2017 Oct 17.Learn from microbial intelligence for avermectins overproduction.1, 2, 3, 4.1Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China.2State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.3Key Biosensor Laboratory of Shandong Province, Biology Institute, Shandong Academy of Sciences, Jinan 250014, China.4State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, C Key Biosensor Laboratory of Shandong Province, Biology Institute, Shandong Academy of Sciences, Jinan 250014, C Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266061, China. Electronic address: lxzhang@ecust.edu.cn.AbstractMicrobial strains are amazingly clever by homeostasis of their own survival and optimization for the overproduction of a desired phenotype, for example drugable secondary metabolites through coordination of key genes overexpression and media optimizations. Besides their pesticide activities, avermectins (AVMs) are identified as potent antibiotic agents for a wide range of drug-resistant pathogens by a high-throughput synergy screening strategy. To rewire the genetic circuitry controlling low yields, we summarized the work on balancing the biological chassis with functional parts, and optimized their dynamical process, as well as predicted favorable effective overproduction of AVMs by 5Ms strategy. AVMs are exclusively made in China now and intelligences learned from the success of AVMs will help transform microbes into a true power-house of innovation.PMID:
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英语作文Learn from others
200字左右，好的话追加分。
那个，我们学校老师第一天开学提到过，大学就是个交流的平台，这个我想加进去。麻烦再看看了。 谁想要300分的？回答满意的可以到去一起领。如果我满意就给400分。
Whoever has the advantages, strengths, but also have shortcomings, weaknesses, all need to learn from others with an open mind so that people check to make up their own short, can progress. Very important to learn from others, in many details of life can be found, always have the opportunity to learn. And failed to learn not only missed the opportunity to learn. To learn from others is not a disgrace, a disgrace that they do not understand quite understand it loaded like to ask others disdain. 无论是谁，都有优点、长处，也都有缺点、短处，都需要虚心向别人学习，做到取人之长，补己之短，才会有进步。向别人学习很重要，在很多生活细节中可以发现，时时刻刻都有学习的机会。不是学不到，只是错过了学的机会。向别人学习并不丢脸，丢脸的是自己不明白却装得很懂的样子，不屑于问别人。 有不对的地方，请多多宽容，谢谢！！
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Learning From Others Part of being human is to learn from one another. Knowledge, skills, and even philosophy are passed on not only from generation to generation, but from culture to culture. The primary culture in North America was derived from the British Isles and, later, various parts of Continental Europe. Since the early 20th Century we in North America have felt that we have all the answers and will lead the way without a sideways glance. This has accelerated since World War II and, indeed, we and much of the rest of the world seems to assume that the North American &Coca-Cola Cowboy& culture will naturally take over all other cultures. This culture promotes commercial consumption, unbridled materialism and a &don't-fence-me-in& attitude toward traditions and restraint. However, at the end of the 20th Century, we are hitting up against boundaries and boundaries are not an agreeable concept in North America. During the 1987-88 school year our family went to Bavaria to live so that our boys could learn to speak German before they were twelve.years old. Birgit, my wife, was born in Germany, and her father had made a deal with all of his grandchildren that they would get a small reward if they could speak a second language by the age of twelve. Any language would do and we picked German. That year was a great eye-opener for me. We found ourselves living in a milieu where respect seemed to be an important element.....respect for the landscape, for nature, for heritage, for the family and for future generations. Respect is not a word that springs to mind in the 20th Century. In fact, the United States was founded on disrespect. From the Boston Tea Party to the cowboys and on to Rambo.... American heroes are revered for their disrespect. Europe has had many centuries to work itself through that adolescent, disrespectful phase. Europe has very painfully learned to live with boundaries. In my view, the more sophisticated and mature philosophy of Northwest Europe represents the best hope for the future of North America and, indeed, the world. We must learn to live with boundaries, restraint and respect. If not, human societies and nature will be devastated in a very short time. During our year in Bavaria, we had a strong feeling that Germany has much to teach North America about new ways to move into a kinder, gentler, more sustainable future. It is possible to harmonize modern technology and respect for natural and human heritage. German-American studies are a two way street. We, on this side of the Atlantic, can no longer afford this 20th Century self-centred myopia. North America needs to look beyond Beethoven and Goethe to possible models for the future. And Germany needs to look beyond the &Coca-Cola-Cowboy& culture to more profound American values.翻译一部分人是被相互学习。 Knowledge, skills, and even philosophy are passed on not only from generation to generation, but from culture to culture.知识，技能，甚至是通过对哲学不仅代代相传，而且从文化的文化。 The primary culture in North America was derived from the British Isles and, later, various parts of Continental Europe.原代培养在北美来自不列颠群岛，后来，各部分欧洲大陆。 Since the early 20th Century we in North America have felt that we have all the answers and will lead the way without a sideways glance.自20世纪初，我们在北美已经认为我们拥有所有的答案，并将率先没有横向一目了然。 This has accelerated since World War II and, indeed, we and much of the rest of the world seems to assume that the North American &Coca-Cola Cowboy& culture will naturally take over all other cultures.这加快了自第二次世界大战结束以来，事实上，我们和许多其他地方的世界似乎认为北美“可口可乐牛仔”的文化自然会接管所有其他文化。 This culture promotes commercial consumption, unbridled materialism and a &don't-fence-me-in& attitude toward traditions and restraint.这种文化促进商业消费，无节制的唯物论和“别，围栏，我的”态度，传统和克制。 However, at the end of the 20th Century, we are hitting up against boundaries and boundaries are not an agreeable concept in North America.然而，在20世纪末，我们是打了对边界和界限不是同意的概念在北美地区。 During the 1987-88 school year our family went to Bavaria to live so that our boys could learn to speak German before they were twelve.years old. Birgit, my wife, was born in Germany, and her father had made a deal with all of his grandchildren that they would get a small reward if they could speak a second language by the age of twelve.在87年至88年学年我们全家去了德国巴伐利亚州的生活，使我们的孩子能够学会讲德语，然后再twelve.years岁。比尔吉德，我的妻子，出生在德国，和她的父亲提出了处理所有他的孙子，他们将得到一个小奖励，如果他们会说第二语言的年龄为12 。 Any language would do and we picked German.任何一种语言将尽我们选择德国。 That year was a great eye-opener for me.这一年是一个伟大的大开眼界，对我来说。 We found ourselves living in a milieu where respect seemed to be an important element.....respect for the landscape, for nature, for heritage, for the family and for future generations. Respect is not a word that springs to mind in the 20th Century.我们发现自己生活在一个环境下尊重似乎是一个重要组成部分.....尊重自然景观，自然的，因为遗产，为家庭和后代。尊重是不是一个词，泉水想到的20世纪。 In fact, the United States was founded on disrespect.事实上，美国是建立在不尊重。 From the Boston Tea Party to the cowboys and on to Rambo....从波士顿倾茶事件的牛仔和对兰博.... American heroes are revered for their disrespect.美国英雄的崇敬他们的不尊重。 Europe has had many centuries to work itself through that adolescent, disrespectful phase.欧洲有许多世纪的工作本身，青少年，不敬阶段。 Europe has very painfully learned to live with boundaries.欧洲有非常痛苦的经验教训一起生活的界限。 In my view, the more sophisticated and mature philosophy of Northwest Europe represents the best hope for the future of North America and, indeed, the world.在我看来，更先进的，成熟的理念，西北欧洲的最好对未来的希望北美，乃至全世界。 We must learn to live with boundaries, restraint and respect.我们必须学会生活的界限，克制和尊重。 If not, human societies and nature will be devastated in a very short time.如果不是这样，人类社会与自然的破坏将是在很短的时间。 During our year in Bavaria, we had a strong feeling that Germany has much to teach North America about new ways to move into a kinder, gentler, more sustainable future.在我们的一年，在德国巴伐利亚州，我们有一个强烈的感觉，德国有很多教北美洲的新方式进入仁慈和温和的，更可持续的未来。 It is possible to harmonize modern technology and respect for natural and human heritage.这是可能使现代技术和尊重自然和人类文化遗产。 German-American studies are a two way street.德美研究双向街。 We, on this side of the Atlantic, can no longer afford this 20th Century self-centred myopia.我们在此方面的大西洋，再也不能这20世纪以自我为中心的近视。 North America needs to look beyond Beethoven and Goethe to possible models for the future. And Germany needs to look beyond the &Coca-Cola-Cowboy& culture to more profound American values.北美需求超越贝多芬和歌德的可能模式的未来。和德国需要超越“可口可乐，牛仔”文化，以更深刻的美国价值观。
How can we learn English well at college? Firstly, you should try your best to enlarge your vocabulary. Only in this way can you read more smoothly and understand others well. Secondly, you should pay more attention to the listening and speaking ability in learning English. While talking with foreigners, if you can’t understand them, and do not know how to express yourself, the talking will be very difficult. And the last point is that you should improve your English whenever and wherever you can. Obviously, there is still a long way for us to go to learn English well. As a proverb says, Never too old to learn.’ There are so many methods to learn English. So long as you keep on studying, your English will surely be improved. 4.Learning English at College 1. 在大学里学习英语不同于在中学里学英语。 2. 通过两年的学习，我有了一些学好英语的体会。 3. 然而，要想学好英语，道路还很漫长。 Learning English at a college is different from learning English at a middle school. In a middle school, we learn English mainly for entrance examination for college, while the purpose of learning English at a college is quite different. We study foreign languages now to improve our ability to work well in the future and especially to commnicate with foreigners. Having been studying here for nearly 2 years, I have come to some conclusions about how to learn Enlgish well. Firstly, you should try your best to enlarge your vocabulary, only in this way can you read more and understand others ms well. Secondly, listening and speaking play important roles in learning English. While talking with foreigners, if you can't understand them, how can you express your ideas? And the last point is that you should improve your English level whenever you can. However, there is still a long way to go to learn English well. As people usually say, &There is no end to learning&. There are .so many methods to learn English. So long as you keep studying, you will surely improve. 在大学里学英语 在大学里学习英语不同于在中学里学英语。在中学里，我们学英语主要为高考，而在大学里学英语情况就不同了。我们现在学英语是为了提高能力以便将来好好工作，尤其是同外国人交流。 通过两年的学习，我有了一些学好英语的体会。首先，你应当努力扩大你的词汇，只有这样才能广泛阅读，明白人家的意思。其次，听和说在英语学习中起着重要作用。同外国人交谈时，如果你不懂人家的意思，你又怎能表达自己的思想呢? 最后一点是，你要抓住任何机会提高你的英语水平。 然而，要学好英语，道路还很漫长。正如人们常说的：“学无止境。”学习英语的方法很多，只要你坚持学，你一定会提高你的英语水平。 We were born to learn from others. Since we were babies, we have been imitating and learning from our parents and other people. Learning from others is an instinct of human beings. We have been surviving because we have learned some basic skills, of which we might have never second thought in our life. As the world keeps changing quickly, it's our basic instinct to learn the new knowledge that we need from others' experience.
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个人意见，o(∩_∩)o...哈哈
Learn from others
◆If you can oftenly deal with the supereminence peoples，and please pay attention to him/her，you will be success。.Pay attention to the supereminence people，this the best for learning from others.
◆Espial the people who is well you feel and learn from him/her. Learn the excellence from them，if you find someone，most of us think he/her is good at action annd behave！What is attracting me from him/her？What is letting me have a good impress for him/her？I think，there will have many reason caused one people attracted you.Such as condescension, very positive attitude,gentlest body language,beautiful wear.Once you known these,you need to learn these from him/her immediately!
We were born to learn from others. Since we were babies, we have been imitating and learning from our parents and other people. Learning from others is an instinct of human beings. We have been surviving because we have learned some basic skills, of which we might have never second thought in our life. As the world keeps changing quickly, it's our basic instinct to learn the new knowledge that we need from others' experience.
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我们会通过消息、邮箱等方式尽快将举报结果通知您。英语作文:learn to respect our parents 20个词左右_百度知道
英语作文:learn to respect our parents 20个词左右
我有更好的答案
Learn to Respect Our ParentsTeenagers should respect their parents.In fact,some children can't respect their parents.The reason that they don't respect their parents is they can't understand their parents well.For example they will feel that they are not free under their parents control.If parents care for them more times,they will be bored.I think no matter how it is,we should respect parents.Because parents' only aim is to be good for us.So,from now on let's take it into practice to respect our parents.First,we must listen to parents and understand them.We can't confute them,but we can explain our ideas patiently.Second,we should help our parents as possible as we can.Because our parents are too tired.So we must try our best to help them.Third,we should work hard to reward our parents in the future.Let's carry forward the virtue of respecting parents.-------------------------------------------------------------------------------------------------------------------你认可我的回答么~~~如有不懂，可以追问~~~^.^
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In general, parents are the first teacher of the children. They give us life. Whatever we shall do in the future, we must respect our parents.
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happiness!!@~@手打的，不要辜负啦O(∩_∩)O~
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我们会通过消息、邮箱等方式尽快将举报结果通知您。Established: August 3, 2016
The Machine Learning Group at Microsoft Research Asia pushes the frontier of machine learning from theoretic, algorithmic, and practical aspects. Our current research focus is on deep/reinforcement learning, distributed machine learning, and graph learning. Other research projects from our group include learning to rank, computational advertising, and cloud pricing. We have published many highly-cited papers on top conferences and journals, helped our partner product groups apply machine learning to large and complex tasks, and open-sourced Microsoft Distributed Machine Learning Toolkit (DMTK) and Microsoft Graph Engine.
微软亚洲研究院机器学习组在理论、算法、应用等不同层面推动机器学习领域的学术前沿。我们目前的研究重点为深度学习/增强学习、分布式机器学习和图学习。我们的研究课题还包括排序学习、计算广告和云定价。在过去的十几年间，我们在顶级国际会议和期刊上发表了大量高质量论文，帮助微软的产品部门解决了很多复杂问题，并向开源社区贡献了微软分布式机器学习工具包（DMTK）和微软图引擎，并受到广泛关注。
Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011.
[Journal Papers]
Shuaiqiang Wang, Shanshan Huang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen, Ranking-oriented Collaborative Filtering: A Listwise Approach, ACM Transactions on Information Systems, 2016
Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, and Bo Zheng, Efficient Mechanism Design for Online Scheduling, Journal of Artificial Intelligence Research, 2016.
Chang Xu, Gang Wang, Xiaoguang Liu, Tie-Yan Liu, Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks, IEEE Transactions on Computers, 2016.
Wei Chen, Tie-Yan Liu, and Xinxin Yang, Reinforcement Learning Behaviors in Sponsored Search, Applied Stochastic Models in Business and Industry, 2016.
Qing Cui, Bin Gao, Jiang Bian, Hanjun Dai, and Tie-Yan Liu, KNET: A General Framework for Learning Word Embedding using Morphological Knowledge, ACM Transactions on Information Systems, 2015.
Wei Wei, Bin Gao, Tie-Yan Liu, Taifeng Wang, Guohui Li, and Hang Li, A Ranking Approach on Large-scale Graph with Multi-dimensional Heterogeneous Information, IEEE Transactions on Cybernetics, 2015.
Tao Qin, Wei Chen, and Tie-Yan Liu, Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology, 2014.
Ying Zhang, Weinan Zhang, Bin Gao, Xiaojie Yuan, and Tie-Yan Liu, Bid Keyword Suggestion in Sponsored Search based on Competitiveness and Relevance, Information Processing and Management, 2014.
Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Support Systems, 2013
Bin Gao, Tie-Yan Liu, Yuting Liu, Taifeng Wang, Zhiming Ma, and Hang Li, Page Importance Computation based on Markov Processes, Information Retrieval, 2011.
Olivier Chapelle, Yi Chang, and Tie-Yan Liu, Future research directions on learning to rank, Proceeding track, Journal of Machine Learning Research, 2011.
Xiubo Geng, Tie-Yan Liu, Tao Qin, Xueqi Cheng, Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
Yin He and Tie-Yan Liu, Tendency Correlation Analysis for Direct Optimization of Evaluation Measures in Information Retrieval, Information Retrieval, 2010.
Tie-Yan Liu, Thorsten Joachims, Hang Li, and Chengxiang Zhai, Introduction to special issue on learning to rank for information retrieval, Information Retrieval, 2010.
Tie-Yan Liu. Learning to Rank for Information Retrieval, Foundations and Trends in Information Retrieval, 2009.
Yuting Liu, Tie-Yan Liu, Zhiming Ma, and Hang Li. A Framework to Compute Page Importance based on User Behaviors, Information Retrieval, 2009.
Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval, 2009.
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, 2009
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. Query-level Loss Function for Information Retrieval. Information Processing and Management, 2007.
Tao Qin, Xu-Dong Zhang, Tie-Yan Liu, De-Sheng Wang, Hong-Jiang Zhang. An Active Feedback Framework for Image Retrieval, Pattern Recognition Letters, 2007.
Ying Bao, Guang Feng, Tie-Yan Liu, Zhiming Ma and Ying Wang. Ranking Websites: A Probabilistic View, Internet Mathematics, 2007.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Guang Geng, De-Sheng Wang, and Wei-Ying Ma. Topic Distillation Via Subsite Retrieval, Information Processing and Management, 2006.
Bin Gao, Tie-Yan Liu, Xin Zheng, Qiansheng Cheng, and Wei-Ying Ma. Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Co-partitioning, IEEE Transactions on Knowledge and Data Engineering, 2005.
Tie-Yan Liu, Yiming Yang, Hao Wan, Hua-Jun Zeng, Zheng Chen, and Wei-Ying Ma. Support Vector Machines Classification with Very Large Scale Taxonomy, SIGKDD Explorations, 2005.
Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. A New Cut Detection Algorithm with Constant False-Alarm Ratio for Video Segmentation, Journal of Visual Communications and Image Representation, 2004.
Tie-Yan Liu, Xu-Dong Zhang, Jian Feng, and Kwoktung Lo. Shot Reconstruction Degree: a Novel Criterion for Key Frame Selection, Pattern Recognition Letters, 2004.
Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. Frame Interpolation Scheme Using Inertia Motion Prediction. Signal Processing: Image Communication, 2003.
Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. Inertia-based Cut Detection and Its Integration with Video Coder. IEE Proceedings on Vision, Image and Signal Processing, 2003.
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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.
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Yue Wang, Wei Chen, Yuting Liu, and Tie-Yan Liu, Finite Sample Analysis of GTD Policy Evaluation Algorithms in Markov Setting, NIPS 2017,
Yingce Xia, Tao Qin, Wei Chen, Tie-Yan Liu, Dual Supervised Learning, ICML 2017.
Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, and Tie-Yan Liu, Asynchronous Stochastic Gradient Descent with Delay Compensation, ICML2017.
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.
Quanming Yao, James Kwok, Fei Gao, Wei Chen, and Tie-Yan Liu, Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems, IJCAI 2017.
Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction, AAAI 2017.
Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Generalization Error Bounds for Optimization Algorithms via Stability, AAAI 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, AAAI2017.
Shizhao Sun, Wei Chen, Jiang Bian, and Tie-Yan Liu, Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks, ECML2017.
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Xiang Li, Tao Qin, and Tie-Yan Liu, 2-Component Recurrent Neural Networks, NIPS 2016
Di He, Yingce Xia, Tao Qin, Tie-Yan Liu, and Wei-Ying Ma, Machine Translation Through Learning From a Communication Game, NIPS 2016
Qi Meng, Guolin Ke, Qiwei Ye, Taifeng Wang, Wei Chen, and Tie-Yan Liu, PV-Tree: A Communication-Efficient Parallel Algorithm for Decision Tree, NIPS 2016
Huazheng Wang, Fei Tian, Bin Gao, Chenjieren Zhu, Jiang Bian, Tie-Yan Liu, Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding, EMNLP 2016.
Yiren Wang, Fei Tian, Recurrent Residual Learning for Sequence Classification,
EMNLP 2016, short paper.
Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu and Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays, IJCAI 2016.
Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang and Tie-Yan Liu, Asynchronous Accelerated Stochastic Gradient Descent, IJCAI 2016.
Hongbin Ma, Bin Shao, Yanghua Xiao, Liang Jeff Chen, Haixun Wang. G-SQL: Fast Query Processing via Graph Exploration. PVLDB 2016.
Yingce Xia, Tao Qin, 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.
Bo Zheng, Li Xiao, Guang Yang, Tao Qin, Online Posted-Price Mechanism with a Finite Time Horizon, AAMAS 2016, short paper.
Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View, AAAI 2016.
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, Competitive Pricing for Cloud Computing in an Evolutionary Market, IJCAI 2015.
Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, and 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.
Yitan Li, Linli Xu, Fei Tian, Liang Jiang, Xiaowei Zhong and Enhong Chen, Word Embedding Revisited: A New Representation Learning and Explicit Matrix Factorization Perspective, IJCAI 2015.
Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, and Jari Veijalainen, Listwise Collaborative Filtering, SIGIR 2015.
Binyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals, AAMAS 2015.
Jiang Rong, Tao Qin, and Bo An. Quantal Response Equilibrium for Sponsored Search Auctions, AAMAS 2015, short paper.
Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Xing, Tie-Yan Liu, and Wei-Ying Ma, LightLDA: Big Topic Models on Modest Computer Cluster, WWW 2015.
Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, Senior Member Track, AAAI 2015.
Haifang Li, Wei Chen, Fei Tian, Tao Qin, and Tie-Yan Liu, Generalization Analysis for Game-theoretic Machine Learning, AAAI 2015.
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.
Liang He, Bin Shao, Yatao Li, Enhong Chen. Distributed Real-Time Knowledge Graph Serving. BigComp 2015. Invited Paper.
Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, and Tie-Yan Liu, A General Approach to Incorporate Knowledge into Word Representation, CIKM 2014.
Fei Tian, Jiang Bian, Bin Gao, Hanjun Dai, Rui Zhang, and Tie-Yan Liu, A Scalable Probabilistic Model for Learning Multi-Prototype Word Embedding, COLING 2014.
Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, and Tie-Yan Liu, Co-learning of Word Representations and Morpheme Representations, COLING 2014.
Bin Gao, Jiang Bian, and Tie-Yan Liu, Knowledge Powered Deep Learning for Word Embedding, ECML/PKDD 2014.
Junpei Komiyama and Tao Qin, Time-Decaying Bandits for Non-stationary Systems, WINE 2014.
Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang, Generalized Second Price Auction with Probabilistic Broad Match, EC 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.
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.
Fei Tian, Bin Gao and Tie-Yan Liu, Learning Deep Representations for Graph Clustering, AAAI 2014.
Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang and Tie-Yan Liu, Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks, AAAI 2014.
Tie-Yan Liu, Weidong Ma, Tao Qin, and Tao Wu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014.
Jiang Bian, Taifeng Wang, and Tie-Yan Liu, Sampling Dilemma: Towards Effective Data Sampling for Click Prediction in Sponsored Search, WSDM 2014.
Lu Wang, Yanghua Xiao, Bin Shao, Haixun Wang, How to Partition a Billion-Node Graph ICDE 2014.
Huanhuan Xia, Tun Lu, Bin Shao, Guo Li, Xianghua Ding, Ning Gu, A partial Replication Approach for Anywhere Anytime Mobile Commenting
CSCW 2014.
Zichao Qi, Yanghua Xiao, Bin Shao, Haixun Wang. Toward a Distance Oracle for Billion-Node Graphs. PVLDB 2014.
Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu, A Theoretical Analysis of NDCG Type Ranking Measures, COLT 2013.
Weihao Kong, Jian Li, Tie-Yan Liu and Tao Qin, Optimal Allocation for Chunked-Reward Advertising, WINE 2013.
Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
Taifeng Wang, Jiang Bian, Shusen Liu, Yuyu Zhang, and Tie-Yan Liu, Psychological Advertising: Exploring Consumer Psychology for Click Prediction in Sponsored Search, KDD 2013.
Bin Shao, Haixun Wang, Yatao Li, Trinity: A Distributed Graph Engine on a Memory Cloud, SIGMOD 2013.
Kai Zeng, Jiacheng Yang, Haixun Wang, Bin Shao, Zhongyuan Wang, A Distributed Graph Engine for Web Scale RDF Data, PVLDB 2013.
Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search, IJCAI 2013.
Wenkui Ding, Tao Qin, and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
Haifeng Xu, Diyi Yang, Bin Gao and Tie-Yan Liu, Predicting Advertiser Bidding Behaviors in Sponsored Search by Rationality Modeling, WWW 2013.
Lei Yao, Wei Chen and Tie-Yan Liu, Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction, WINE 2012.
Weinan Zhang, Ying Zhang, Bin Gao, Yong Yu, Xiaojie Yuan, and Tie-Yan Liu, Joint optimization of bid and budget allocation in sponsored search, KDD 2012.
Chenyan Xiong, Taifeng Wang, Wenkui Ding, Yidong Shen, Tie-Yan Liu. Relational Click Prediction for Sponsored Search, WSDM 2012.
Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-Yan Liu, Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. NIPS 2012.
Zhao Sun, Hongzhi Wang, Haixun Wang, Bin Shao, and Jianzhong Li. Efficient Subgraph Matching on Billion Node Graphs. PVLDB 2012
Bin Gao, Tie-Yan Liu, Taifeng Wang, Wei Wei, and Hang Li, Semi-supervised graph ranking with rich meta data, KDD 2011
Zhicong Cheng, Bin Gao, Congkai Sun, Yanbing Jiang, and Tie-Yan Liu. Let Web Spammers Expose Themselves, WSDM 2011.
Zhicong Cheng, Bin Gao, and Tie-Yan Liu, Actively Predicting Diverse Search Intent from User Browsing Behaviors, WWW 2010.
Tao Qin, Xiubo Geng, and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
Wei Chen, Tie-Yan Liu, Zhiming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, NIPS 2010.
Jiang Bian, Tie-Yan Liu, Tao Qin, and Hongyuan Zha, Query-dependent Loss Function for Web Search. WSDM 2010.
Fen Xia, Tie-Yan Liu, Hang Li, Statistical Consistency of Top-k Ranking, NIPS 2009.
Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li, Ranking Measures and Loss Functions in Learning to Rank, NIPS 2009.
Yanyan Lan, Tie-Yan Liu, Zhiming Ma, and Hang Li. Generalization Analysis for Listwise Learning to Rank Algorithms, ICML 2009.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008.
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. Listwise Approach to Learning to Rank: Theory and Algorithm, ICML 2008.
Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, and Hang Li. Query-level Stability and Generalization in Learning to Rank, ICML 2008.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wen-Ying Xiong, and Hang Li. Learning to Rank Relational Objects and Its Application to Web Search, WWW 2008.
Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, and Heung-Yeung Shum. Query-dependent Ranking using K-Nearest Neighbor, SIGIR 2008.
Yuting Liu, Bin Gao, Tie-Yan Liu, Ying Zhang, Zhiming Ma, Shuyuan He, and Hang Li. BrowseRank: Letting Web Users Vote for Page Importance, SIGIR 2008.
Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. Directly Optimizing IR Evaluation Measures in Learning to Rank, SIGIR 2008.
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
Yuting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, and Hang Li. Supervised Rank Aggregation, WWW 2007.
Xiubo Geng, Tie-Yan Liu, Tao Qin, and Hang Li. Feature Selection for Ranking, SIGIR 2007.
Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
Guang Feng, Tie-Yan Liu, Ying Wang, Ying Bao, Zhiming Ma, Xu-Dong Zhang, and Wei-Ying Ma. AggregateRank: Bringing Order to Websites, SIGIR 2006.
Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, and Hsiao-Wuen Hon. Adapting Ranking SVM to Document Retrieval, SIGIR 2006.
Qiankun Zhao, Chuhong Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, and Wei-Ying Ma. Time-Dependent Semantic Similarity Measure of Queries Using Historical Click-Through Data, WWW 2006.
Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, and Wei-Ying Ma. Event Detection from Evolution of Click-through Data, KDD 2006.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, and Wei-Ying Ma. A Study on Relevance Propagation for Web Search, SIGIR 2005.
Bin Gao, Tie-Yan Liu, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. Consistent Bipartite Graph Co-Partitioning for Star-Structured High-Order Heterogeneous Data Co-Clustering, KDD 2005.
Bin Gao, Tie-Yan Liu, Tao Qin, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. Web Image Clustering by Consistent Utilization of Visual Features and Surrounding Texts, ACM Multimedia 2005.
Tie-Yan Liu, Tao Qin and Hong-Jiang Zhang. Time-constraint Boost for TV Commercials Detection. IEEE ICIP 2004.
Bin Gao, Tie-Yan Liu, Qian-Sheng Cheng, and Wei-Ying Ma. A Linear Approximation Based Method for Noise-Robust and Illumination-invariant Image Change Detection. PCM 2004.
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