Zhai, Ke
kezhai AT microsoft.com [
CV
]
I am currently a senior research scientist in Microsoft.
Research Interests
Statistical Machine Learning
Large-scale: Distributed and Online Learning
Probabilistic Bayesian Models
Education
I received Bachelor of Engineering from
School of Computer Engineering
,
Nanyang Technological University, Singapore
in 2009, under the supervision of
Dr. Wee Keong Ng
on topic of
Privacy-Preserving Data Mining
.
My undergaduate research thesis was "An Embedded Caching Framework for Privacy-Preserving Data Mining".
I received Master of Science from
Department of Computer Science
,
University of Maryland, College Park
in 2011, under the supervision of
Dr. Jordan Boyd-Graber
and
Dr. Jimmy Lin
(co-supervised) on topic of
Variational Bayesian Inference of Latent Dirichlet Allocation in MapReduce
.
My master research scholary paper was "Using Variational Inference and MapReduce to Scale Topic Models".
I received my Ph.D. from
Department of Computer Science
,
University of Maryland, College Park
.
My advisor is
Dr. Jordan Boyd-Graber
, and co-supervised by
Dr. Jimmy Lin
.
I am associated with
University of Maryland Institute for Advanced Computer Studies
,
Cloud Computing Center
and
Computational Linguistics and Information Processing
.
I am interested in machine learning, non-parametric Bayesian learning and cloud computing.
publications
(* indicates equal contribution)
Ke Zhai
*, and Huan Wang*.
Adaptive Dropout with Rademacher Complexity Regularization
.
International Conference on Learning Representations
(ICLR-2018), May 2018.
[Code & Data]
Ke Zhai
, Zornitsa Kozareva, Yuening Hu, Qi Li and Weiwei Guo.
Query to Knowledge: Unsupervised Entity Extraction from Shopping Queries using Adaptor Grammars
.
International ACM SIGIR Conference on Research and Development in Information Retrieval
(SIGIR-2016), Jul 2016.
[Slide]
Zornitsa Kozareva, Qi Li,
Ke Zhai
and Weiwei Guo.
Recognizing Salient Entities in Shopping Queries
.
Association for Computational Linguistics
(ACL-2016), Jun 2016.
Ke Zhai
, Jordan Boyd-Graber and Shay B. Cohen.
Online Adaptor Grammars with Hybrid Inference
.
Transaction of the Association for Computational Linguistics
(TACL), Oct 2014.
[Code & Data]
Ke Zhai
and Jason D. Williams.
Discovering Latent Structure in Task-Oriented Dialogues
.
Association for Computational Linguistics
(ACL-2014), Jun 2014.
[Slide]
Ke Zhai
*, Yuening Hu*, Vladimir Edelman and Jordan Boyd-Graber.
Polylingual Tree-Based Topic Models for Translation Domain Adaptation
.
Association for Computational Linguistics
(ACL-2014), Jun 2014.
Ke Zhai
and Jordan Boyd-Graber.
Online Latent Dirichlet Allocation with Infinite Vocabulary
.
International Conference on Machine Learning
(ICML-2013), Jun 2013.
[Code & Data]
Ke Zhai
*, Yuening Hu*, Sinead Williamson and Jordan Boyd-Graber.
Modeling Images using Transformed Indian Buffet Processes
.
International Conference on Machine Learning
(ICML-2012), Jun 2012.
[Code]
[Data]
Ke Zhai
, Jordan Boyd-Graber, Nima Asadi and Mohamed Alkhouja.
Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce
.
ACM International Conference on World Wide Web
(WWW-2012), Apr 2012.
[Code]
Ke Zhai
, Wee Keong Ng, Andre Ricardo Herianto and Shuguo Han.
Speeding Up Secure Computations via Embedded Caching
.
Proceedings of SIAM International Conference on Data Mining
(SDM-2009), Apr 2009.