Manuscripts

. Randomization Inference When N Equals One. arXiv:2310.16989, 2023.

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. Detecting Weak Distribution Shifts via Displacement Interpolation. arXiv:2305.15609, 2023.

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. Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. arXiv:2212.02457, 2022.

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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

. 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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. A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers. Annals of Statistics, 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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. 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:

  • 41000 (MBA): Business Statistics: Winter 24, Winter 23, Fall 21, Fall 20, Fall 19, Fall 18, Spring 18

  • 41918 (PhD): Data, Learning, and Algorithms: Winter 24

    • Lecture 1: (Randomized) Dimension Reduction: randomized linear algebra, dimension reduction, and data visualization. Python Notebook
    • Lecture 2: Resampling and Inference: computer age inference, simulation-based and resampling method.
    • Lecture 3: What If: causal inference in theory and practice, causal structural models, do-calculus, adjustments formula using supervised learning.
    • Lecture 4: No Regret: online algorithms and optimization, sequential investments, universal portfolios. Python Notebook
    • Lecture 5: Explore vs. Exploit I: sequential decision making, dynamic programming and its approximations, and reinforcement learning techniques including Q-learning and policy gradients.
    • Lecture 6: Explore vs. Exploit II: stochastic and adversarial bandits, exploration and exploitation, upper confidence bound algorithm, explore-then-commit algorithm, exponential weight algorithm.

  • Voluntary (PhD): Reading Group

Wharton:

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

Professional Service

Workshops & Talks

More Talks

Academia Sinica
Aug 7, 2023
Seminar, Institute of Statistical Science
LSE
May 22, 2023
Data Science Seminar, Dept. of Statistics
UCSD
May 16, 2023
Econometrics Seminar, Dept. of Economics
Cornell
Apr 26, 2023
Statistics Seminar, Dept. of Statistics and Data Science
UPenn
Nov 30, 2022
Statistics Seminar, Dept. of Statistics, The Wharton School of Business
Princeton
Nov 28, 2022
Wilks Seminar Series, Dept. of Operations Research and Financial Engineering
UW Madison
Nov 16, 2022
Statistics Seminar, Dept. of Statistics
UCLA
Sep 28, 2022
Econometrics Seminar, Dept. of Economics

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