Manuscripts

. Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. arXiv:2004.04767, 2020.

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. A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers. arXiv:2002.01586, 2020.

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. Estimating Certain Integral Probability Metrics (IPMs) Is as Hard as Estimating under the IPMs. arXiv:1911.00730, 2019.

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. On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. arXiv:1811.03179, 2018.

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. Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. arXiv:1809.09953, 2018.

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Publications

. On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. Conference on Learning Theory (COLT), 2020.

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. Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. Journal of the American Statistical Association (Theory and Methods), 2020.

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. Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize. Annals of Statistics, 2020.

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. Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. Journal of Machine Learning Research, 2020.

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. Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2019.

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. Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

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. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

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. Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. Conference on Learning Theory (COLT), 2018.

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. Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. International Conference on Machine Learning (ICML), 2017.

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. Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix. Annals of Statistics, 2017.

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. On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Transactions on Network Science and Engineering, 2017.

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. Geometric Inference for General High-Dimensional Linear Inverse Problems. Annals of Statistics, 2016.

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. Learning with Square Loss: Localization through Offset Rademacher Complexity. Conference on Learning Theory (COLT), 2015.

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. Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. Conference on Learning Theory (COLT), 2015.

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. Law of Log Determinant of Sample Covariance Matrix and Optimal Estimation of Differential Entropy for High-Dimensional Gaussian Distributions. Journal of Multivariate Analysis, 2015.

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Workshops & Talks

More Talks

JSM 2020
Aug 5, 2020
Theoretical Advances in Deep Learning, IMS Invited Session
Google Research NYC
Jun 12, 2020
Learning Theory Seminar
PDF
Duke
Mar 25, 2020
TRIPODS Workshop on Theory and Modeling of Deep Learning
PDF
MIT
Jan 27, 2020
MIFODS Workshop on Learning with a Complex Structure
Harvard
Sep 16, 2019
Statistics Colloquium, Dept. of Statistics
Yale
Sep 11, 2019
Econometrics Seminar, Dept. of Economics
PDF
MIT
Sep 6, 2019
Statistics and Stochastics Seminar Series, IDSS
UChicago
May 1, 2019
Machine Learning Seminar, TTIC
CREST
Mar 25, 2019
Statistics Seminar, ENSAE-CREST
DALI 2019
Jan 5, 2019
Machine Learning Theory Invited Session
PDF

Teaching

Booth:

Wharton:

  • 622 (MBA): Advanced Quantitative Modeling: Spring 15, Spring 14

Awards

  • George C. Tiao Faculty Fellowship, 2017-now
    for research in computational and data science

  • J. Parker Memorial Bursk Award, 2016
    for excellence in research

  • US Junior Oberwolfach Fellow, 2015

  • Winkelman Fellowship, 2014-2017
    the highest honorific fellowship awarded by the Wharton School

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