Weihao Kong
Research Scientist
Email: kweihao at gmail dot com

I am currently a Research Scientist at Google. Previously I was a postdoctoral researcher at University of Washington working with Sham M. Kakade. I got my PhD in 2019 from Stanford University advised by Gregory Valiant. My thesis can be found here. I got my bachelor degree from Shanghai Jiao Tong University and did undergraduate research with Wu-Jun Li.

Research interests:

Recent talks:

CV · Teaching · Talks


  1. Robust and Differentially Private Mean Estimation
    Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh.
    In submission.

    First differentially private robust mean estimation algorithm for high dimensional distributions.

  2. SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics
    Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh.
    To appear in ICML 2021.
  3. Online Model Selection for Reinforcement Learning with Function Approximation
    Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill.
    AISTATS 2021

    A meta-algorithm that adapts to the optimal model complexity with regret bound $\tilde{O}(d_*T^{2/3})$.

  4. Robust Meta-learning for Mixed Linear Regression with Small Batches [Video]
    Weihao Kong, Raghav Somani, Sham M. Kakade, Sewoong Oh.
    NeurIPS 2020

    Smooth time/sample complexity trade-off between the number of tasks and the dataset size of each task. Algorithmic contribution includes an optimal robust PCA algorithm.

  5. Meta-Learning for Mixed Linear Regression [Video]
    Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh.
    ICML 2020

    First poly time/sample complexity algorithm for $k$-mixture linear regression by utilizing a small set of tasks each with $\tilde{O}(\sqrt{k})$ examples.

  6. Optimal Estimation of Change in a Population of Parameters
    Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade.

    Estimate the distribution of treatment effects.

  7. Sublinear Optimal Policy Value Estimation in Contextual Bandits [Video]
    Weihao Kong, Gregory Valiant, Emma Brunskill.
    AISTATS 2020.

    Estimate optimal policy value of contextual bandits with $K$ disjoint $d$-dimensional arms in $\tilde{O} (\sqrt{d}K)$ rounds, minimax optimal up to log factors.

  8. Maximum Likelihood Estimation for Learning Populations of Parameters
    Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade.
    ICML 2019.

    $n$ different coins, each tossed $t$ times, estimate distribution of coin biases with Wasserstein error $O(1/\sqrt{t\log n})$ when $n<2^t$, minimax optimal.

  9. Efficient Algorithms and Lower Bounds for Robust Linear Regression
    Ilias Diakonikolas*, Weihao Kong*, Alistair Stewart*.
    SODA 2019

    Solve $\epsilon$ corrupted linear regression up to $\tilde{O}(\epsilon)$ error under gaussianity assumption, minimax optimal, previous work achieves $\sqrt{\epsilon}$.

  10. Estimating Learnability in the Sublinear Data Regime [Code]
    Weihao Kong, Gregory Valiant.
    NeurIPS 2018

    Estimate optimal prediction error of linear regression/classification with $O(\sqrt{d})$ examples, minimax optimal.

  11. Approximating the Spectrum of a Graph [Code][Video]
    David Cohen-Steiner*, Weihao Kong*, Christian Sohler*, Gregory Valiant*.
    KDD 2018.

    Approximate graph Laplacian specturm in constant time.

  12. Recovering Structured Probability Matrices
    Qingqing Huang*, Sham M. Kakade*, Weihao Kong*, Gregory Valiant*.
    ITCS 2018.
  13. Learning Populations of Parameters [Video]
    Kevin Tian, Weihao Kong, Gregory Valiant.
    NeurIPS 2017.

    $n$ different coins, each tossed $t$ times, estimate distribution of coin biases with Wasserstein error $O(1/{t})$ when $n\ge 2^t$, minimax optimal.

  14. Spectrum Estimation from Samples [Code]
    Weihao Kong, Gregory Valiant.
    Annals of Statistics, 2017.

    First algorithm consistently estimates eigenvalues of covariance with $o(d)$ samples.

  15. Optimal Allocation for Chunked-Reward Advertising
    Weihao Kong, Jian Li, Tao Qin, Tie-Yan Liu.
    WINE 2013.
  16. Isotropic Hashing [Code]
    Weihao Kong, Wu-Jun Li.
    NeurIPS 2012.[Poster]
  17. Manhattan Hashing for Large-Scale Image Retrieval [Code]
    Weihao Kong, Wu-Jun Li, Minyi Guo.
    SIGIR 2012.
  18. Double-bit quantization for hashing [Code]
    Weihao Kong, Wu-Jun Li.
    AAAI 2012.
(asterisk indicates alphabetical authorship)