Full Day Tutorial

Practice of Efficient Data Collection via Crowdsourcing: Aggregation, Incremental Relabelling, and Pricing

Alexey DrutsaYandex, Russia

Valentina FedorovaYandex, Russia

Dmitry UstalovYandex, Russia

Olga MegorskayaYandex, Russia

Evfrosiniya ZerminovaYandex, Russia

Daria BaidakovaYandex, Russia

Topic: To be announced.

Half-Day Tutorials

Deep Bayesian Data Mining

Jen-Tzung ChienNational Chiao Tung University, Taiwan

Topic: This tutorial addresses the advances in deep Bayesian data mining for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network, sequence-to-sequence model, variational auto-encoder (VAE), generative adversarial network, attention mechanism, memory-augmented neural network, skip neural network, temporal difference VAE, stochastic neural network, stochastic temporal convolutional network, predictive state neural network, and policy neural network. Enhancing the prior/posterior representation is addressed. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in sequence data. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies are presented to tackle different issues in deep Bayesian mining and understanding. At last, we will point out a number of directions and outlooks for future studies.

Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments

Somit GuptaMicrosoft, USA

Xiaolin ShiSnap, USA

Pavel DmitrievMicrosoft, USA

Xin FuFacebook, USA

Topic: To be announced.

Intelligible Machine Learning and Knowledge Discovery Boosted By Visual Means

Boris KovalerchukCentral Washington University, USA

Topic: To be announced.

Learning and Reasoning on Graph for Recommendation

Xiang WangNational University of Singapore, Singapore

Xiangnan HeUniversity of Science and Technology of China, China

Tat-Seng ChuaNational University of Singapore, Singapore

Topic: To be announced.

Learning with Small Data

Zhenhui LiPennsylvania State University, USA

Huaxiu YaoPennsylvania State University, USA

Fenglong MaPennsylvania State University, USA

Topic: To be announced.

Deep Learning for Anomaly Detection

Ruoying WangLinkedIn, USA

Kexin NieLinkedIn, USA

Tie WangLinkedIn, USA

Yang YangLinkedIn, USA

Bo LongLinkedIn, USA

Topic: To be announced.

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Felice Antonio MerraPolytechnic University of Bari, Italy

Tommaso Di NoiaPolytechnic University of Bari, Italy

Yashar DeldjooPolytechnic University of Bari, Italy

Topic: To be announced.

Web-Scale Knowledge Collection

Colin LockardUniversity of Washington, USA

Prashant ShiralkarAmazon, USA

Xin Luna DongAmazon, USA

Hannaneh HajishirziUniversity of Washington, USA

Topic: To be announced.


Tutorial Chairs

Questions about the WSDM tutorials should be directed to: wsdm-2020-tutorials-chairs@googlegroups.com