Welcome!

My name is Zi WANG, and I am a PhD student at EECS, MIT and part of LIS at CSAIL. I am working with Prof. Stefanie Jegelka, Prof. Leslie Pack Kaelbling, and Prof. Tomás Lozano-Pérez on exciting projects about active learning and Bayesian optimization with an ultimate goal of embedding the learning abilities in robots that help with household chores.

More about me

News


Recent Talks


  • [Aug 9, 2017] Max-value Entropy Search for Efficient Bayesian Optimization @ ICML 2017, Sydney, Australia. [slides]
  • [Aug 7, 2017] Batched High-dimensional Bayesian Optimization via Structural Kernel Learning @ ICML 2017, Sydney, Australia. [slides]
  • [Jun 20, 2017] (remote talk) Scaling up Bayesian Optimization with Ensembles @ DeepMind, London, UK. [slides]
  • [Jun 16, 2017] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems @ Uber ATG, Pittsburgh, PA.
  • [Jun 7, 2017] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems @ The Manipulation Lab, CMU Robotics Institute, Pittsburgh, PA.
  • [May 31, 2017] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems @ ICRA 2017, Singapore. [poster][slides]
  • [Apr 28, 2017] Learning a "Few-shot" Precondition Generator @ LIS, MIT, Cambridge, MA.
  • [May 9, 2016] Optimization as Estimation with Gaussian Processes in Bandit Settings @ AISTATS 2016, Cadiz, Spain. [slides]
  • [May 2, 2016] Optimization as Estimation with Gaussian Processes in Bandit Settings @ Machine Learning Tea, MIT, Cambridge, MA.
  • Research Interest

    Learning and planning, and how they interact.


    Education

    Massachusetts Institute of Technology

    2014 - present

    Ph.D. student in Electrical Engineering and Computer Science
    Advisors: Prof. Stefanie Jegelka, Prof. Leslie Kaelbling, and Prof. Tomás Lozano-Pérez

    Massachusetts Institute of Technology

    2014 - 2016

    S.M. in Electrical Engineering and Computer Science
    Master thesis: Optimization as Estimation with Gaussian Process Bandits
    Thesis Supervisors: Prof. Stefanie Jegelka and Prof. Leslie Kaelbling

    Tsinghua University

    2010 - 2014

    B.Eng. in Computer Science and Technology
    Undergraduate thesis: Fast Dropout Training for Deep Neural Networks (in Chinese)
    Thesis advisors: Prof. Fei Sha and Prof. Jun Zhu


    Publications

    Preprint


    Ensemble Bayesian Optimization

    Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka

    arXiv:1706.01445

    Abstract arXiv BibTex

    2017


    Batched High-dimensional Bayesian Optimization via Structural Kernel Learning

    Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli

    International Conference on Machine Learning (ICML), 2017

    Abstract arXiv Code Slides BibTex

    Max-value Entropy Search for Efficient Bayesian Optimization

    Zi Wang and Stefanie Jegelka

    International Conference on Machine Learning (ICML), 2017

    Abstract arXiv Code Slides BibTex

    Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems

    Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tomás Lozano-Pérez

    IEEE Conference on Robotics and Automation (ICRA), 2017

    Abstract PDF arXiv Video Slides BibTex

    2016


    Optimization as Estimation with Gaussian Processes in Bandit Settings

    Zi Wang, Bolei Zhou, and Stefanie Jegelka

    International Conference on Artificial Intelligence and Statistics (AISTATS), 2016

    Oral presentation (6% acceptance rate)

    Abstract PDF arXiv Code Project page Slides BibTex

    2014


    Fast Learning with Noise in Deep Neural Nets

    Zhiyun Lu*, Zi Wang*, and Fei Sha

    (Spotlight Presentation) NIPS Workshop: Perturbations, Optimization, and Statistics, Quebec, Canada, 2014

    Abstract PDF BibTex

    Discriminative Non-Negative Matrix Factorization for Single-Channel Speech Separation

    Zi Wang and Fei Sha

    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 2014

    Abstract PDF Project page IEEE Poster BibTex

    2013


    Scalable Inference for Logistic-Normal Topic Models

    Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, and Bo Zhang

    Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, CA, 2013

    Abstract PDF Project page NIPS Poster Code BibTex

    Contact Me

    ziw 'at' mit.edu

    To family and friends who think computer science is about making websites and fixing your computer: yes, I am physically able to do these, but they are not intellectually challenging enough for me, as an AI major. Please consult the people who study networks and architecture on the 7th floor of Stata (JK!).