. Interpolating Classifiers Make Few Mistakes. arXiv:2101.11815, 2021.

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. Deep Learning for Individual Heterogeneity. arXiv:2010.14694, 2020.



More Publications

. Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. Journal of the American Statistical Association (Theory and Methods), 2021.

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. Deep Neural Networks for Estimation and Inference. Econometrica, 2021.

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


  • NSF CAREER Award, 2021-2026

  • 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

Professional Service

Workshops & Talks

More Talks

Mar 10, 2021
UMass Amherst
Mar 5, 2021
NSF-Simons Collaboration, Mathematics of Deep Learning
Dec 16, 2020
JSM 2020
Aug 5, 2020
Google Research NYC
Jun 12, 2020
Mar 25, 2020
Jan 27, 2020
Sep 16, 2019
Sep 11, 2019
Sep 6, 2019