Publications

. Reversible Gromov-Monge Sampler for Simulation-Based Inference. SIAM Journal on Mathematics of Data Science, 2023.

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. High-Dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. Information and Inference: A Journal of the IMA, 2023.

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. Interpolating Classifiers Make Few Mistakes. Journal of Machine Learning Research, 2023.

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. Online Learning to Transport via the Minimal Selection Principle. Conference on Learning Theory (COLT), 2022.

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. Universal Prediction Band via Semi-Definite Programming. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022.

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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), 2022.

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

Booth:

Wharton:

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

Professional Service

Workshops & Talks

More Talks

Academia Sinica
Aug 7, 2023
LSE
May 22, 2023
UCSD
May 16, 2023
Cornell
Apr 26, 2023
UPenn
Nov 30, 2022
Princeton
Nov 28, 2022
UW Madison
Nov 16, 2022
UCLA
Sep 28, 2022

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