Women can easily be too involved in the extravagant nightlife of Dubai and not have the chance to find a woman. Find a reliable guide on the web. Simply a couple of clicks on their website will take you to profiles of different ladies, and then you are able to send them an email to schedule meetings. There’s no need to fret about lengthy discussions or pricey drinks, as you might in expensive clubs. Additionally, you don’t need to spend time on dates when you are able to meet a gorgeous lady in the escorts in dubai privacy of your own home.

If you’re looking for an escort within Dubai You can locate an escort in a variety of locations within the city. Typically, escorts will be given to you and will guide you through the areas that are most well-known. If you are visiting the St. George Hotel, its lobby at the Hyatt Regency Hotel is known as a”red light” district during the night. You can select an escort which will meet all of your requirements and wow the business partners you have with.

You can find the most exotic entertainer, or Dubai escorts. Although this can be a great method to get to know a stranger, it could take a lot of time and energy. It’s not necessary to stand in line for your turn. Instead, enjoy your evening with friends and avoid those awkward moments when meeting someone for the first time. It’s an excellent method to make your trip memorable. An escort can help you have an unforgettable experience.

The professors gave us a group project to complete in 2 days, and we had 3 of them to complete. Then one of us recommended seeking help from do my homework . Thankful to the quick service of DoMyHomework, we could submit our work on time and even got good grades.

Qing Da (Q. Da) 中文简历

csdaqing@gmail.com / linkedin / Google scholar / DBLP

I received my B.Sc. degree and M.Sc. degree from Nanjing University, China in 2010 and 2013 respectively. Since Sep. 2013, I become a Ph.D. student in the LAMDA Group, Nanjing University, under the supervision of Prof. Zhi-Hua Zhou.

In Jan. 2015 (my 2nd Ph.D. year), I quit the Ph.D. program and joined the search algorithm team at Alibaba. Now I am a Senior Staff Algorithm Engineer of Alibaba Group.

Research Interests

My research interests include machine learning, data mining and their applications in search, recommendations and advertisement.

Career

taxi lyon

Jan 2015 – Jun 2016: Algorithm Engineer II (资深算法工程师), Search Department, Alibaba Group
Jul 2016 – Dec 2017: Senior Algorithm Engineer (算法专家), Search Department, Alibaba Group
Jan 2018 – Jul 2020: Staff Algorithm Engineer (高级算法专家), AI International Department, Alibaba Group
Aug 2020 – Present: Senior Staff Algorithm Engineer (资深算法专家), AE Tech Department, Alibaba Group.

Books

Qing Da, An-Xiang Zeng, Reinforcement Learning Beyond Games: To Make a difference in Alibaba. Electronic Industry Press, 2018. Link

Environments

1. VirtualTaobao[AAAI’19], Github.

This project provides VirtualTaobao simulators trained from the real-data of Taobao, one of the largest online retail platforms. In Taobao, when a customer entered some query, the recommondation system returns a list of items according to the query and the customer profile. The system is expected to return a good list such that customers will have high chances of clicking the items.

Using VirtualTaobao simulator, one can access a “live” environment just like the real Taobao environment. Virtual customers will be generated once at a time, the virtual customer starts a query, and the recommendation system needs to return a list of items. The virtual customer will decide if it would like to click the items in the list, similar to a real customer.

How VirtualTaobao was trained is described in：

Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019. PDF

2. SESim, Github.

SESim is an E-Commerce search engine simulation platform for model examinations, which was a missing piece to connect evaluations of LTR researches and business objectives of real-world applications. SESim can examine models in the simulation E-commerce environment with dynamic responses, and its framework can be easily extended to other scenarios that items and users have differ- ent features. We hope to see the development of a dynamic dataset that facilitates industrial LTR researches in the future.

A typical process of industrial search engines contains three stages to produce a display list from a user query. A search engine first retrieves related items with intend of the user (i.e. the user query), then the ranker ranks these items by a fine-tuned deep LTR model, finally, the re-ranker rearranges the order of items to achieve some businesses goals such as diversity and advertising. Our proposed simulation platform SESim contains these three stages. We replace queries with category indices in our work, therefore SESim can retrieve items from a desensitized items database by the category index. After that, a customizable ranker and a customizable re-ranker produce the final item list. SESim allows us to study joint learning of multiple models, we left it as future work and focus on the correct evaluation for a single model.

Besides the set of real items, two important modules make SESim vividly reflect the behaviors of real users. Virtual user module aims at generating embeddings of virtual users and their query, and it follows the paradigm of Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Feedback module inputs the dis- play list and the information of the user, then outputs the feedback of users on the display list. To model the decision process of users, we train the feedback module by Generative Adversarial Imitation Learning (GAIL). For diversifying behaviors, we consider clicking and purchasing, which are two of the most important feedback of users in E-commerce.

How SESim was trained is described in：

Yongqing Gao, Guangda Huzhang, Weijie Shen, Yawen Liu, Wen-Ji Zhou, Qing Da, Yang Yu. Imitate The World: A Search Engine Simulation Platform. Submitted to CIKM’21, CORR abs/2107.07693.

Publications

Wen-Ji Zhou, Yunan Ye, Qing Da, Yinfu Feng, Anxiang Zeng, Han Yu and Chunyan Miao. Policy Gradient Matching for Recommendation Systems. Submitted to CIKM’21.
Chenlin Shen, Guangda Huzhang, Yu-Hang Zhou, Shen Liang, Qing Da. A General Traffic Shaping Protocol in E-Commerce. Submitted to CIKM’21.
Xuesi Wang, Guangda Huzhang, Qianying Lin, Qing Da. Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce. Submitted to CIKM’21.
Yongqing Gao, Guangda Huzhang, Weijie Shen, Yawen Liu, Wen-Ji Zhou, Qing Da, Yang Yu. Imitate The World: A Search Engine Simulation Platform. Submitted to CIKM’21, CORR abs/2107.07693.
Wenya Zhu, Yinghua Zhang, Yu Zhang, Yu-Hang Zhou, Yinfu Feng, Qing Da, Yuxiang Wu and Xiaoyu Lv. DHA: Product Title Generation with Discriminative Hierarchical Attention for E-commerce. Submitted to CIKM’21.
Wenya Zhu, Xiaoyu Lv, Baosong Yang, Yinghua Zhang, xu yong, Dayiheng Liu, Linlong Xu, yinfu feng, Haibo Zhang,Qing Da and Weihua Luo. CLPR-9M: An E-Commerce Dataset for Cross-Lingual Product Retrieval. Submitted to EMNLP’21.
Junmei Hao, Jingcheng Shi, Qing Da, Anxiang Zeng, Yujie Dun, Xueming Qian, Qianying Lin. Diversity Regularized Interests Modeling for Recommender Systems. Submitted to TMM. CORR abs/2103.12404
Yanshi Wang, Jie Zhang, Qing Da, Anxiang Zeng. Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction, 2020, CORR abs/2011.11826
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu, Weijie Shen, Wen-Ji Zhou, Qianying Lin, Qing Da, An-Xiang Zeng, Han Yu, Yang Yu, Zhi-Hua Zhou. AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online. IEEE Transactions on Knowledge and Data Engineering (TKDE). CORR abs/2003.11941
Yu-Hang Zhou, Peng Hu, Chen Liang, Huan Xu, Guangda Huzhang, Yinfu Feng, Qing Da, XInshang Wang, An-Xiang Zeng. A Primal-Dual Online Algorithm for Online Matching Problem in Dynamic Environments. In: Proceedings of the 34rd AAAI Conference on Artificial Intelligence (AAAI-21). PDF
Anxiang Zeng, Han Yu, Qing Da, Yusen Zhan, Chun-yanMiao, Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection. In: Proceedings of the 34rd AAAI Conference on Artificial Intelligence (AAAI-20 / IAAI-20), New York, USA, 2020. PDF
Pengcheng Li, Runze Li, Qing Da, An-Xiang Zeng, Lijun Zhang. Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space. In: Proceedings of the 29th International Conference on Information and Knowledge Management (CIKM’20), Virtual Event, Ireland, 2020. PDF
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019. PDF
Feiyang Pan, Qingpeng Cai , An-Xiang Zeng , Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang. Policy Optimization with Model-based Explorations. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019. PDF
Hua-Lin Hei, Chun-Xiang Pan, Qing Da, An-Xiang Zeng. Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design. In: “Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’18)“, Dublin, Ireland, 2018. PDF
Shi-Yong Chen, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang and Hai-Hong Tang. Stablizing reinforcement learning in dynamic environment with application to online recommendation. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18) (Research Track), London, UK, 2018. PDF
Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, Yinghui Xu, Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18) (Applied Track), London, UK, 2018. PDF
Yang Yu, Shi-Yong Chen, Qing Da, Zhi-Hua Zhou. Reusable reinforcement learning via shallow trails. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(6): 2204-2215. PDF
Yang Yu, Peng-Fei Hou, Qing Da, and Yu Qian. Boosting nonparametric policies. In: Proceedings of the 2016 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’16), Singapore, 2016, pp.477-484. PDF
Yang Yu and Qing Da, PolicyBoost: Functional policy gradient with ranking-based reward objective. In: Proceedings of AAAI Workshop on AI and Robotics (AIRob’14), Quebec City, Canada, 2014. PDF
Qing Da, Yang Yu, and Zhi-Hua Zhou. Learning with Augmented Class by Exploiting Unlabeled Data. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’14), Québec city, Canada, 2014. PDF
Qing Da, Yang Yu, and Zhi-Hua Zhou. Napping for Functional Representation of Policy. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’14), Paris, France, 2014. PDF
Qing Da, Yang Yu, and Zhi-Hua Zhou. Self-Practice Imitation Learning from Weak Policy. In: Proceedings of the 2nd IAPR International Workshop on Partially Supervised Learning (PSL’13), Nanjing, China, 2013, pp.9-20. PDF

Teaching Assistant

Awards & Honors

OpenAI Retro Contest (Transfer Learning in Reinforcement learning), Champion, 2018
National Graduate Scholarship, 2012
First prize of Internet contest for Cloud & Mobile computing (for image search track), 2012
Grand Prize Winner of the PAKDD 2012 Data Mining Competition (Open Category) (with Nan Li, Chao Qian, Shao-Yuan Li, Yue Zhu, and Zhi-Hua Zhou), 2012
Outstanding project (six in total over the nation) of China Innovation Program for Students (sponsored by Sun), 2010 (project page)
Computer World Scholarship, 2009
First prize in China Undergraduate Mathematical Contest in Modeling (CUMCM), 2008, pdf.