Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of California - Los Angeles 2021
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  Academic Positions Outside Duke

  • Postdoctoral Fellow, California Institute of Technology. 2021 - 2022
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Cramer, Estee Y., Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, et al. “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.” Proc Natl Acad Sci U S A 119, no. 15 (April 12, 2022): e2113561119. https://doi.org/10.1073/pnas.2113561119.
       Full Text  Open Access Copy  Link to Item
    • Zhou, D., P. Xu, and Q. Gu. “Stochastic nested variance reduction for nonconvex optimization.” Journal of Machine Learning Research 21 (May 1, 2020).
    • Zhou, D., P. Xu, and Q. Gu. “Stochastic variance-reduced cubic regularization methods.” Journal of Machine Learning Research 20 (August 1, 2019).

  • Conference Papers

    • Zou, D., P. Xu, and Q. Gu. “Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling.” In 37th Conference on Uncertainty in Artificial Intelligence, Uai 2021, 1152–62, 2021.
    • Jin, T., P. Xu, Q. Gu, and F. Farnoud. “Rank aggregation via heterogeneous thurstone preference models.” In Aaai 2020  34th Aaai Conference on Artificial Intelligence, 4353–60, 2020.
    • Wu, Y., W. Zhang, P. Xu, and Q. Gu. “A finite-time analysis of two time-scale actor-critic methods.” In Advances in Neural Information Processing Systems, Vol. 2020-December, 2020.
    • Xu, P., and Q. Gu. “A finite-time analysis of Q-Learning with neural network function approximation.” In 37th International Conference on Machine Learning, Icml 2020, PartF168147-14:10486–96, 2020.
    • Zou, D., P. Xu, and Q. Gu. “Sampling from non-log-concave distributions via stochastic variance-reduced gradient Langevin dynamics.” In Aistats 2019  22nd International Conference on Artificial Intelligence and Statistics, 2020.
    • Xu, P., F. Gao, and Q. Gu. “An improved convergence analysis of stochastic variance-reduced policy gradient.” In 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, 2019.
    • Zou, D., P. Xu, and Q. Gu. “Stochastic gradient hamiltonian monte carlo methods with recursive variance reduction.” In Advances in Neural Information Processing Systems, Vol. 32, 2019.
    • Chen, J., P. Xu, L. Wang, J. Ma, and Q. Gu. “Covariate adjusted precision matrix estimation via nonconvex optimization.” In 35th International Conference on Machine Learning, Icml 2018, 2:1464–89, 2018.
    • Xu, P., D. Zou, J. Chen, and Q. Gu. “Global convergence of Langevin dynamics based algorithms for nonconvex optimization.” In Advances in Neural Information Processing Systems, 2018-December:3122–33, 2018.
    • Xu, P., T. Wang, and Q. Gu. “Accelerated stochastic mirror descent: From continuous-time dynamics to discrete-time algorithms.” In International Conference on Artificial Intelligence and Statistics, Aistats 2018, 1087–96, 2018.
    • Xu, P., T. Wang, and Q. Gu. “Continuous and discrete-time accelerated stochastic mirror descent for strongly convex functions.” In 35th International Conference on Machine Learning, Icml 2018, 12:8738–51, 2018.
    • Yu, Y., P. Xu, and Q. Gu. “Third-order smoothness helps: Faster stochastic optimization algorithms for finding local minima.” In Advances in Neural Information Processing Systems, 2018-December:4525–35, 2018.
    • Zhou, D., P. Xu, and Q. Gu. “Stochastic nested variance reduction for nonconvex optimization.” In Advances in Neural Information Processing Systems, 2018-December:3921–32, 2018.
    • Zhou, D., P. Xu, and Q. Gu. “Stochastic variance-reduced cubic regularized Newton method.” In 35th International Conference on Machine Learning, Icml 2018, 13:9597–9606, 2018.
    • Zou, D., P. Xu, and Q. Gu. “Subsampled stochastic variance-reduced gradient langevin dynamics.” In 34th Conference on Uncertainty in Artificial Intelligence 2018, Uai 2018, 1:508–18, 2018.
    • Zou, D., P. Xu, and Q. Gu. “Stochastic variance-reduced Hamilton Monte Carlo methods.” In 35th International Conference on Machine Learning, Icml 2018, 13:9647–56, 2018.
    • Chaudhry, A., P. Xu, and Q. Gu. “Uncertainty assessment and false discovery rate control in high-dimensional Granger causal inference.” In 34th International Conference on Machine Learning, Icml 2017, 2:1120–55, 2017.
    • Xu, P., J. Ma, and Q. Gu. “Speeding up latent variable Gaussian graphical model estimation via nonconvex optimization.” In Advances in Neural Information Processing Systems, 2017-December:1934–45, 2017.
    • Xu, P., T. Zhang, and Q. Gu. “Efficient algorithm for sparse tensor-variate gaussian graphical models via gradient descent.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Aistats 2017, 2017.
    • Tian, L., P. Xu, and Q. Gu. “Forward backward greedy algorithms for multi-task learning with faster rates.” In 32nd Conference on Uncertainty in Artificial Intelligence 2016, Uai 2016, 735–44, 2016.
    • Xu, P., and Q. Gu. “Semiparametric differential graph models.” In Advances in Neural Information Processing Systems, 1072–80, 2016.

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