Oscar Hernan Madrid Padilla

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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:

A copy of my CV can be found here.

I was born and raised in Honduras.

Grants:

NSF DMS-2015489

Published/Accepted papers

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.

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). PDF.

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. PDF.

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

O.-H. Madrid-Padilla, Y. Yu, A. Rinaldo. Lattice partition recovery with dyadic CART. 35th Conference on Neural Information Processing Systems. 2021. PDF.

O.H. Madrid-Padilla, Sabyasachi Chatterjee. Risk Bounds for Quantile Trend Filtering. To appear in Biometrika. PDF.

O.-H. Madrid-Padilla and Y. Chen. Graphon estimation via nearest neighbor algorithm and 2D fused lasso denoising. To appear in The Canadian Journal of Statistics. Link.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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

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

Y.L Kei, Y. Chen, O.-H. Madrid-Padilla. A Partially Separable Temporal Model for Dynamic Valued Networks. PDF.

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

G. Ruiz, O.-H. Madrid-Padilla, Q. Zhou. Sequential Learning of the Topological Ordering for the Linear Non-Gaussian Acyclic Model with Parametric Noise. PDF.

O.-H. Madrid-Padilla, S. Chatterjee. Quantile Regression by Dyadic CART. PDF.

S. Ye, Y. Chen, O.-H. Madrid-Padilla. Non-parametric interpretabe score based estimation of heterogeneous treatment effects. PDF. Code.

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

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

L. Cappello, O.-H. Madrid-Padilla, J. A. Palacios. Scalable Bayesian change point detection with spike and slab priors. PDF.

Y. Yu, O.-H. Madrid-Padilla, D. Wang, A. Rinaldo. Optimal network online change point localisation. PDF.

O.H. Madrid-Padilla, W. Tansey, Y. Chen. Quantile regression with ReLU Networks: Estimators and minimax rates. PDF.

Alfonso Landeros, O.H. Madrid-Padilla, Hua Zhou, Kenneth Lange. Extensions to the Proximal Distance of Method of Constrained Optimization. PDF.

Yi Yu, O.-H. Madrid-Padilla, Daren Wang, Alessandro Rinaldo. A Note on Online Change Point Detection. PDF.

Alexandre Belloni* , Mingli Chen* , O. H. Madrid-Padilla* , Zixuan (Kevin) Wang* . High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing. Link

Marcos Matabuena , O. H. Madrid-Padilla . Energy distance and kernel mean embeddings for two-sample survival testing. Link

O.-H. Madrid-Padilla, Yi Yu, Carey E. Priebe. Change point localization in dependent dynamic nonparametric random dot product graphs. Link

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

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

*Alphabetical order.

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