Oscar Hernan Madrid Padilla

Welcome!

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:

• High-dimensional and nonparametric statistics
• Change point detection
• Network problems
• Deep learning
• Causal inference
• Quantile regression
• Bayesian and empirical Bayes methodology
• Graphical models

A copy of my CV can be found here.

Email:

oscar dot madrid at stat dot ucla dot edu

Editorial Service:

• Associate Editor for Journal of Journal of the Royal Statistical Society Series B, 2025–present
• Associate Editor for Journal of Computational and Graphical Statistics, 2024–present
• Associate Editor for Stat, 2022–present

Grants:

• NSF DMS-2015489
• UCLA 2022-23 Faculty Career Development Award
• Hellman Fellowship, 2023-2024.

Former Ph.D. students:

• Siwei Ye (Graduated in 2025, Placement: Google)
• Yik Lun (Allen) Kei (Graduated in 2024, Placement: UC Santa Cruz postdoc).
• Gabriel Ruiz (Graduated in 2022, Placement: Adobe).

Current Ph.D. students:

• Davis Berlind
• Kyle Ritscher
• Xiaokai Luo
• Junpeng Ren
• Austin Froelich

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

Hangjian Li, Mahmoud Essalat, Alfonso Landeros.

Published/Accepted papers

1. C.M Madrid-Padilla, O.H. Madrid-Padilla, S. Chatterjee. Risk Bounds For Distributional Regression. To appear in NeurIPS 2025. PDF.

2. C.K. Nguen, O.H. Madrid-Padilla, A.A. Amini. Network two-sample test for block models. To appear in NeurIPS 2025. PDF.

3. Y.L Kei, H. Li, Y. Chen, O.H. Madrid-Padilla. Change Point Detection on a Separable Model for Dynamic Networks. To appear in Transactions on Machine Learning Research. PDF.

4. F. Wang, W. Li, O.H. Madrid-Padilla, Y. Yu, A. Rinaldo. Multilayer random dot product graphs: Estimation and online change point detection. PDF. To appear in Journal of the Royal Statistical Society Series B.

5. Y. L. Kei, J. Li, H. Li, Y. Chen, O.H. Madrid-Padilla. Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model. Transactions on Machine Learning Research, 2025. PDF.

6. L. Cappello, O.H. Madrid-Padilla. Variance change point detection with credible sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 47, No. 6, 2025. PDF. (Impact factor: 20.8). Code.

7. Zhi Zhang, Kyle Ritscher, O.H. Madrid-Padilla. Quantile Additive Trend Filtering. To appear in Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025. PDF.

8. 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. To appear in Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025. PDF.

9. H. Li, O.-H. Madrid-Padilla, Q. Zhou. Learning Gaussian DAGs from Network Data. Journal of Machine Learning Research, 25(377):1−52, 2024. PDF.

10. M. Matabuena, J.C. Vidal, O.-H. Madrid-Padilla, D. Sejdinovic. Kernel Biclustering algorithm in Hilbert Spaces. Advances in Data Analysis and Classification (2025): 1-42. PDF.

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

12. 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

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

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

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

21. 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).

22. 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.

23. 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

24. 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.

25. 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.

26. 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.

27. 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.

28. 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.

29. 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

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

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

32. 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.

33. 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.

34. 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.

35. 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.

36. 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.

37. 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.

38. 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.

39. 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.

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

41. 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.

42. 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.

43. 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. Y.L Kei, O.H. Madrid-Padilla, R. Killick, J. Wilson, X. Chen, R. Lund. Clustering in Networks with Time-varying Nodal Attributes. PDF.

2. S. Kumar, H. Xu, C.M. Madrid-Padilla, Y. Khoo, O.H. Madrid-Padilla, D. Wang. Bias-variance Tradeoff in Tensor Estimation. PDF.

3. D. Berlind, L. Cappello, O.H. Madrid-Padilla. A Bayesian framework for change-point detection with uncertainty. quantification. PDF.

4. F. Wang, K. Ritscher, Y.L. Kei, X. Ma, O.H. Madrid-Padilla. Change Point Localization and Inference in Dynamic Multilayer Networks. PDF.

5. C.M Madrid-Padilla, O.H. Madrid-Padilla, Y.L. Kei, Z. Zhang, Y. Chen. Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks. PDF. (Equal contribution from the first two authors). Code.

6. H. Xu, C.M Madrid-Padilla, O.H. Madrid-Padilla, D. Wang. Multivariate Poisson intensity estimation via low-rank tensor decomposition. PDF.

7. D. Lai, O.H. Madrid-Padilla, T. Guan. Bayesian Transfer Learning for Enhanced Estimation and Inference. PDF. 2025 ASA Student Paper Award.

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

9. 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.

10. 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.

11. 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.

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

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

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

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

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

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

*Alphabetical order.

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