(Adams) Wei Yu
I am a PhD candidate in Machine Learning Department of SCS at CMU, working on large scale optimization, deep learning, statistical machine learning and natural language processing, advised by Jaime Carbonell and Alex Smola. I also belong to the StatML Group.
My research is/was supported by NVIDIA PhD fellowship, CMU Presidential Fellowship and Siebel Scholarship. Thanks!
Email: wei(my last name) AT cs DOT cmu DOT edu;
OR: adams(my last name)wei AT gmail DOT com
Address: GHC 8215, 5000 Forbes Avenue, Pittsburgh, PA 15213.
- Oct 16, 2017:
Preprint DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimizationis available.
- Oct 03, 2017:
Paper accepted to JMLR.
- Sep 27, 2017:
Honored to be named CMU Presidential Fellow!
- Sep 16, 2017:
Preprint on Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks is available.
- Jul 29 - Aug 04, 2017:
Attending ACL in Vancouver.
- Jul 17, 2017:
Preprint on Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training is available.
- May 22, 2017:
Start another internship at Google Brain.
- May 13, 2017:
Awarded NVIDIA PhD Fellowship!
- Apr 20-22, 2017:
Attending AISTATS in Fort Lauderdale.
- Mar 31, 2017:
Paper accepted to ACL.
- Mar 3-5, 2017:
Attending Siebel Scholar Conference 2017, Washington DC.
- Feb 15-17, 2017:
Invited talk at CSL Student Conferences 2017, UIUC.
- Jan 17, 2017:
I am organizing CMU AI Distinguished Lecture Series and Seminar this year. You should give a talk!
- Nov 13-16, 2016:
Invited talk at INFORMS Annual Meeting 2016, Nashville.
- July 17, 2016: I am serving as a workflow chair of AISTATS 2017.
- June 7, 2016:
Preprint on Computationally Feasible Near-Optimal Subset Selection for Linear Regression available.
- June 19-26, 2016:
Attending ICML and COLT in New York.
- May 31, 2016:
Start internship at Google Brain.
- May 9-11, 2016:
Attending AISTATS in Cadiz.
- April 25, 2016:
Paper accepted to COLT.
- April 11, 2016:
Updated version of DSPDC available.
- Mar 30, 2016:
Invited talk at MSR Redmond.
- Mar 17-19, 2016:
Invited talk at INFORMS Optimization Society conference, Princeton.
- Feb 23, 2016:
Preprint on Improved Gap-Dependency Analysis of the Noisy Power Method is available.
- Dec 22, 2015:
Paper accepted to AISTATS 2016.
- Nov 1-4, 2015:
Invited talk at INFORMS Annual Meeting 2015, Philadelphia.
- Aug 23-28, 2015:
Attending 3rd Heidelberg Laureate Forum as selected young researcher worldwide in math and cs!
- Aug 20, 2015:
Preprints on two efficient algorithms DSPDC and AdaDelay are available!
- Aug 14, 2015:
Finished my fantastic internship in Machine Learning Group of MSR Redmond!
- Dec 8-13, 2014:
Attending NIPS 2014 in Montreal.
- Nov, 2014:
Attending INFORMS Annual Meeting 2014 in San Francisco.
- Sep, 2014:
One paper accepted to NIPS 2014!
- Sep, 2014:
I am named a Siebel Scholar of Class 2015!
- Sep, 2014:
Attending VLDB 2014 in Hangzhou.
- Aug, 2014:
Our ICML 2014 paper is selected in the finalist of INFORMS Data Mining Best Student Paper Award and will be presented in INFORMS Annual Meeting 2014.
- Jun, 2014:
The code of our ICML 2014 paper is released. Feel free to try!
- Jun, 2014:
Attending ICML 2014 in Beijing.
Selected Industrial Internship
- 2017.5 -- 2017.8, Research Intern, Google Brain. Mentor: Quoc Le and Kai Chen.
- 2016.5 -- 2016.8, Research Intern, Google Brain and Web Answers. Mentor: Quoc Le and Hongrae Lee.
- 2015.5 -- 2015.8, Research Intern, Machine Learning Group, Microsoft Research Redmond. Mentor: Lin Xiao.
Journal and Conference:
"DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization".
Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen.
"Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training".
Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime Carbonell.
"Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem".
Adams Wei Yu, Qihang Lin, Tianbao Yang.
"Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks". (Full paper, to appear)
Lei Huang, Xianglong Liu, Bo Lang, Adams Wei Yu, Bo Li.
In AAAI 2018.
"On Computationally Tractable Selection of Experiments in Regression Models". (Accepted, to appear)
Yining Wang, Adams Wei Yu, Aarti Singh.
"Learning to Skim Text". (Long Paper)
Adams Wei Yu, Hongrae Lee, Quoc Le.
In ACL 2017.
"An Improved Gap-Dependency Analysis of the Noisy Power Method". (Full oral)
Maria Florina Balcan**, Simon S. Du**, Yining Wang**, Adams Wei Yu**. (** α-β order)
In COLT 2016.
"Adadelay: Delay Adaptive Distributed Stochastic Optimization".
Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola.
In AISTATS 2016.
"Efficient Structured Matrix Rank Minimization".
Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra.
In NIPS 2014.
"Saddle Points and Accelerated Perceptron Algorithms".
(Full oral, INFORMS Data Mining Best Student Paper Finalist)
Adams Wei Yu, Fatma Kılınç-Karzan, Jaime G. Carbonell.
In ICML 2014.
"Reverse Top-k Search using Random Walk with Restart". (Full oral)
Adams Wei Yu, Nikos Mamoulis, Hao Su.
In VLDB 2014.
"Efficient Euclidean Projections onto the Intersection of Norm Balls". (Full oral)
Adams Wei Yu*, Hao Su*, Li Fei-Fei. (* Indicates equal contribution)
In ICML 2012.
"Risk Assessment and Adaptive Group Testing of Semantic Web Services".
Xiaoying Bai, Ron S. Kenett, Wei Yu.
International Journal of Software Engineering and Knowledge Engineering, 22(5): 595-620, 2012.
"Search by Mobile Image Based on Visual and Spatial Consistency".
(Oral, Best Paper Nomination)
Xianglong Liu, Yihua Lou, Adams Wei Yu, Bo Lang.
In ICME 2011.
"Music Identification via Vocabulary Tree with MFCC Peaks."
Tianjing Xu, Adams Wei Yu, Xianglong Liu, Bo Lang.
In Workshop on Music Information Retrieval with User-centered and Multimodal Strategies, ACM Multimedia, Scottsdale, 2011.
"AUDR: An Advanced Unstructured Data Repository."
Xianglong Liu, Bo Lang, Wei Yu, Junwu Luo, Lei Huang.
In ICPCA 2011.
- CMU Presidential Fellow, 2017-2018.
- NVIDIA PhD Fellowship (11 selected worldwide), 2017.
- Invited young researcher (200 selected worldwide) in Math and CS to attend Heidelberg Laureate Forum, 2015.
- Siebel Scholar (85 selected worldwide), class of 2015.
- INFORMS data mining best student paper finalist, 2014.
- CMU research fellowship, 2013-present.
- HKU postgraduate scholarship for mphil student, 2011-2013.
- ICME 2011 best paper award nomination, 2011.
- China national scholarship, twice.
- Organizing Committee: Workflow Chair, AISTATS 2017.
- Journal Reviewer: JMLR, VLDBJ, TKDE, Neurocomputing.
- Regular Conference Program Committee (Reviewer): ICML, NIPS, KDD, AISTATS, AAAI, ALT.
- Student Coordinator: CMU AI Distinguished Lecture Series and Seminar.
- CMU MLD PhD admission committee.