Manuscripts

. Reversible Gromov-Monge Sampler for Simulation-Based Inference. arXiv:2109.14090, 2021.

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. Universal Prediction Band via Semi-Definite Programming. arXiv:2103.17203, 2021.

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

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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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. Deep Learning for Individual Heterogeneity: An Automatic Inference Framework. arXiv:2010.14694, 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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Publications

More Publications

. How Well Generative Adversarial Networks Learn Distributions. Journal of Machine Learning Research, 2021.

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

Booth:

Wharton:

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

Awards

  • NSF CAREER Award, 2021-2026

  • David G. Booth Faculty Fellow, William S. Fishman Faculty Scholar, 2021-2022

  • George C. Tiao Faculty Fellowship, 2017-2021
    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

UBC
Oct 15, 2021
Sauder School of Business, Operations & Logistics Seminar
ICML 2021 Workshop
Jul 24, 2021
Overparameterization: Pitfalls and Opportunities, Invited Speaker
LSE
Jun 3, 2021
Econometrics Seminar
Durham University Business School
May 27, 2021
Econometrics Seminar
Rutgers
Mar 10, 2021
Statistics Seminar, Dept. of Statistics
UMass Amherst
Mar 5, 2021
Statistics and Probability Seminar, Dept. of Mathematics and Statistics
NSF-Simons Collaboration, Mathematics of Deep Learning
Dec 16, 2020
Mathematics of Deep Learning Workshop
JSM 2020
Aug 5, 2020
Theoretical Advances in Deep Learning, IMS Invited Session
Slides
Google Research NYC
Jun 12, 2020
Learning Theory Seminar
Slides
Duke
Mar 25, 2020
TRIPODS Workshop on Theory and Modeling of Deep Learning
Slides

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