Oscar Hernan Madrid Padilla

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Welcome!

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I am a Tenure-track Assistant Professor in the Department of Statistics at University of California, Los Angeles. Previously, from July, 2017 to June, 2019, I was Neyman Visiting Assistant Professor in the Department of Statistics at University of California, Berkeley. Before that, I earned a Ph.D. in statistics at The University of Texas at Austin in May 2017 under the supervision of Prof. James Scott. My undergraudate degree was a B.S in Mathematics completed at CIMAT (in Mexico) in April 2013, advised by Prof. Daniel Hernandez-Hernandez.

My research interests include:

A copy of my CV can be found here.

Email:

oscar dot madrid at stat dot ucla dot edu

Personal:

I grew up in some of the poorest areas of Honduras. My grandparents were either illiterate or never finished elementary school. My parents did not attend college, but thanks to their tremendous efforts, my brothers Jose (mathematics), Carlos (statistics), and I could become academics. Here is a bio featured in Lathisms 2023.

Editorial Service:

Grants:

Equity diversity and inclusion:

Former Ph.D. students:

Current Ph.D. students:

Former Ph.D. students that I have closely worked with:

Hangjian Li, Mahmoud Essalat, Alfonso Landeros.

Published/Accepted papers

  1. O.-H. Madrid-Padilla. Variance estimation in graphs with the fused lasso. Journal of Machine Learning Research, 25(250):1−45, 2024. PDF.

  2. Marcos Matabuena , O. H. Madrid-Padilla. Energy distance and kernel mean embeddings for two-sample survival testing with application in immunotherapy clinical trial. To appear in REVSTAT. Link

  3. O.-H. Madrid-Padilla, S. Chatterjee. Quantile Regression by Dyadic CART. PDF. Electronic Journal of Statistics, 8(1), 1206-1247, 2024.

  4. Y. Yu, O.-H. Madrid-Padilla, D. Wang, A. Rinaldo. Network online change point localization. PDF. SIAM Journal on Mathematics of Data Science (SIMODS), 6(1), 176-198, 2024.

  5. C.M. Madrid-Padilla, H. Xu, Daren Wang, O.H. Madrid-Padilla, Y. Yu. Change point detection and inference in multivariable nonparametric models under mixing conditions. PDF. Advances in Neural Information Processing Systems (NeurIPS), 36: 21081-21134, 2023.

  6. Yi Yu, O.-H. Madrid-Padilla, Daren Wang, Alessandro Rinaldo. A Note on Online Change Point Detection. PDF. Sequential Analysis, 42(4), 438-471, 2023.

  7. S. Ye, Y. Chen, O.-H. Madrid-Padilla. 2D score based estimation of heterogeneous treatment effects. PDF. Code. Journal of Causal Inference, 11(1), 20220016, 2023.

  8. Y.L Kei, Y. Chen, O.-H. Madrid-Padilla. A Partially Separable Model for Dynamic Valued Networks. PDF. Computational Statistics & Data Analysis, 187, 107811, 2023.

  9. L. Cappello, O.-H. Madrid-Padilla, J. A. Palacios. Bayesian Change Point Detection with Spike and Slab Priors. PDF. Journal of Computational and Graphical Statistics, 32(4), 1488-1500, 2023.

  10. H. Jiang, S. Qin, O.-H. Madrid-Padilla. Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization. PDF. Stat, 12(1), e538, 2023.

  11. Alexandre Belloni* , Mingli Chen* , O. H. Madrid-Padilla* , Zixuan (Kevin) Wang* (alphabetical order). High-Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing. Link. arXiv. Annals of Statistics. 51(1): 96-121 (February 2023).

  12. G. Ruiz, O.-H. Madrid-Padilla, Q. Zhou. Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores. PDF. Transactions on Machine Learning Research 2022.

  13. O.-H. Madrid-Padilla, Yi Yu, Carey E. Priebe. Change point localization in dependent dynamic nonparametric random dot product graphs. Link. Journal of Machine Learning Research, 23(234), 1-59, 2022

  14. O.H. Madrid-Padilla, W. Tansey, Y. Chen. Quantile regression with ReLU Networks: Estimators and minimax rates. PDF. Journal of Machine Learning Research, 23(247), 1−42, 2022. Code.

  15. Alfonso Landeros, O.H. Madrid-Padilla, Hua Zhou, Kenneth Lange. Extensions to the Proximal Distance of Method of Constrained Optimization. PDF. Journal of Machine Learning Research, Vol. 23, No. 182, 1−45, 2022. Code.

  16. M. Essalat, D. Morrison, S. Kak, E. Chang, I. Penso, R. Kulchar, O.-H. Madrid-Padilla, V. Shetty. A naturalistic study of brushing patterns using powered toothbrushes. PLoS One. 2022 May 19;17(5):e0263638. Link.

  17. F. Wang, O.-H. Madrid-Padilla, Y. Yu, A. Rinaldo. Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:4309-4338. (Oral presentation, in the top 44 out of 1685 submissions), 2022. PDF.

  18. Y. Yu, O.-H. Madrid-Padilla, A. Rinaldo. Optimal partition recovery in general graphs. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:4339-4358, 2022. PDF.

  19. O.-H. Madrid-Padilla, Yi Yu, Daren Wang, Alessandro Rinaldo. Optimal nonparametric multivariate change point detection and localization. IEEE Transactions on Information Theory, Volume: 68, Issue: 3, March 2022. PDF

  20. O.-H. Madrid-Padilla, Y. Yu, A. Rinaldo. Lattice partition recovery with dyadic CART. 34th Conference on Neural Information Processing Systems. 2021. PDF.

  21. O.H. Madrid-Padilla, Sabyasachi Chatterjee. Risk Bounds for Quantile Trend Filtering. Biometrika, 109(3), 751-768, 2022. PDF.

  22. O.-H. Madrid-Padilla and Y. Chen. Graphon estimation via nearest-neighbour algorithm and two-dimensional fused-lasso denoising. The Canadian Journal of Statistics, 51(1), 95-110, 2023. Link.

  23. S. Woody, O.-H. Madrid-Padilla, J. G. Scott. Optimal post-selection inference for sparse signals: a nonparametric empirical-Bayes approach. PDF. Biometrika, Volume 109, Issue 1, March 2022, Pages 1–16.

  24. S. Ye, O.H. Madrid-Padilla. Non-Parametric Quantile Regression via the K-NN Fused Lasso. PDF. Journal of Machine Learning Research, Vol. 22, No. 111, 1-38, 2021. Code.

  25. O.-H. Madrid-Padilla, Yi Yu, Daren Wang, Alessandro Rinaldo. Optimal nonparametric change point detection and localization. Electronic Journal of Statistics. 15 (1) 1154 - 1201, 2021. Link.

  26. O.H. Madrid-Padilla, James Sharpnack, Yanzhen Chen, Daniela Witten. Adaptive Non-Parametric Regression With the K-NN Fused Lasso . Biometrika, Volume 107, Issue 2, June 2020, Pages 293–310. Link. Code.

  27. O.H. Madrid-Padilla, Alex Athey, Alex Reinhart, James G. Scott. Sequential nonparametric tests for a change in distribution: an application to detecting radiological anomalies. Journal of the American Statistical Association, Vol. 114, Issue 526, 514-528, 2019. Link.

  28. O.H. Madrid-Padilla, J. Sharpnack, J.G. Scott, and R.J Tibshirani. The DFS Fused Lasso: Linear-Time Denoising over General Graphs. Journal of Machine Learning Research, Vol. 18, No. 176, 1-36, 2018. Link.

  29. O.H. Madrid-Padilla, N.G. Polson and J.G. Scott. A deconvolution path to mixtures. Electronic Journal of Statistics Volume 12, Number 1 (2018), 1717-1751.

  30. D. Hernandez-Hernandez* and O.H. Madrid-Padilla*. Worst portfolios for dynamic monetary utility processes. Stochastics, Vol. 90, Number 1 (2018), 78-101.

  31. O.H. Madrid-Padilla and J.G. Scott. Tensor decomposition with generalized lasso penalties. Journal of Computational and Graphical Statistics 2017, 26:3, 537-546. arXiv. Code.

  32. M. Zhou, O.H. Madrid-Padilla, and J. G. Scott, “Priors for random count matrices derived from a family of negative binomial processes,” Journal of the American Statistical Association 2016, Vol. 111, No. 515, 1144-1156, Theory and Methods. PDF. Code.

  33. W. Tansey, O.-H. Madrid-Padilla, A. Suggala, and P. Ravikumar. Vector-Space Markov Random Fields via Exponential Families.In International Conference on Machine Learning (ICML) 32, 2015. PDF. Code

Preprints

  1. Z. Zhang, C.M Madrid-Padilla, X. Luo, O.H. Madrid-Padilla, D. Wang. Dense ReLU Neural Networks for Temporal-spatial Model. PDF.

  2. Z. Zhang, C. Chow, Y. Zhang, Y. Sun, H. Zhang, E.H Jiang, H. Liu, F. Huang, Y. Cui, O.H. Madrid-Padilla. Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory. PDF.

  3. C.K. Nguen, O.H. Madrid-Padilla, A.A. Amini. Network two-sample test for block models. PDF.

  4. M. Matabuena, R. Ghosal, P. Mozharovskyi, O.H. Madrid-Padilla, J.P. Onnela. Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces. PDF.

  5. M. Matabuena, J.C. Vidal, O.H. Madrid-Padilla, J.P. Onnela. kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection. PDF.

  6. Zhi Zhang, Kyle Ritscher, O.H. Madrid-Padilla. Risk Bounds for Quantile Additive Trend Filtering. PDF.

  7. C.M. Madrid-Padilla, O.H. Madrid-Padilla, D. Wang. Temporal-spatial model via Trend Filtering. (The first author is one of my brothers). PDF.

  8. F. Wang, W. Li, O.H. Madrid-Padilla, Y. Yu, A. Rinaldo. Multilayer random dot product graphs: Estimation and online change point detection. PDF.

  9. Y. L. Kei, J. Li, H. Li, Y. Chen, O.H. Madrid-Padilla. Generative Model for Change Point Detection in Dynamic Graphs. PDF.

  10. M. Essalat, O.H. Madrid-Padilla, V.Shetty, G. Pottie. Monitoring Brushing behaviors using Toothbrush Embedded Motion-Sensors. Link.

  11. Y.L Kei, H. Li, Y. Chen, O.H. Madrid-Padilla. Change Point Detection on a Separable Model for Dynamic Networks. PDF.

  12. L. Cappello, O.H. Madrid-Padilla. Variance change point detection with credible sets. PDF.

  13. O.-H. Madrid-Padilla, Y. Yu. Dynamic and heterogeneous treatment effects with abrupt changes. PDF.

  14. M. Matabuena, J.C. Vidal, O.-H. Madrid-Padilla, D. Sejdinovic. Kernel Biclustering algorithm in Hilbert Spaces. PDF.

  15. G. Ruiz, O.-H. Madrid-Padilla. Non-asymptotic confidence bands on the probability an individual benefits from treatment (PIBT). PDF.

  16. O.-H. Madrid-Padilla, Y. Chen, G. Ruiz. A causal fused lasso for interpretable heterogeneous treatment effects estimation. PDF. Code.

  17. H. Li, O.-H. Madrid-Padilla, Q. Zhou. Learning Gaussian DAGs from Network Data. PDF.

  18. Shitong Wei, O.-H. Madrid-Padilla, James Sharpnack. Distributed Cartesian Power Graph Segmentation for Graphon Estimation. Link.

  19. O.H. Madrid-Padilla and J.G. Scott. Nonparametric density estimation by histogram trend filtering. Link.

*Alphabetical order.

Locations of Site Visitors

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