Vector-Space Markov Random Fields via Exponential Families. - Wesley Tansey; Oscar Hernan Madrid Padilla; Arun Sai Suggala; Pradeep Ravikumar (ICML, 2015)

The DFS Fused Lasso: Linear-Time Denoising over General Graphs. - Oscar Hernan Madrid Padilla; James Sharpnack; James G. Scott; Ryan J. Tibshirani (J. Mach. Learn. Res., 2017)

Distributed Cartesian Power Graph Segmentation for Graphon Estimation. - Shitong Wei; Oscar Hernan Madrid Padilla; James Sharpnack (CoRR, 2018)

Lattice partition recovery with dyadic CART. - Oscar Hernan Madrid Padilla; Yi Yu; Alessandro Rinaldo (NeurIPS, 2021)

Optimal network online change point localisation. - Yi Yu; Oscar Hernan Madrid Padilla; Daren Wang; Alessandro Rinaldo (CoRR, 2021)

Non-parametric Quantile Regression via the K-NN Fused Lasso. - Steven Siwei Ye; Oscar Hernan Madrid Padilla (J. Mach. Learn. Res., 2021)

Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. - Fan Wang; Oscar Hernan Madrid Padilla; Yi Yu; Alessandro Rinaldo (AISTATS, 2022)

Optimal partition recovery in general graphs. - Yi Yu; Oscar Hernan Madrid Padilla; Alessandro Rinaldo (AISTATS, 2022)

Sequential Learning of the Topological Ordering for the Linear Non-Gaussian Acyclic Model with Parametric Noise. - Gabriel Ruiz; Oscar Hernan Madrid Padilla; Qing Zhou (CoRR, 2022)

Extensions to the Proximal Distance Method of Constrained Optimization. - Alfonso Landeros; Oscar Hernan Madrid Padilla; Hua Zhou 001; Kenneth Lange (J. Mach. Learn. Res., 2022)

Quantile regression with ReLU Networks: Estimators and minimax rates. - Oscar Hernan Madrid Padilla; Wesley Tansey; Yanzhen Chen (J. Mach. Learn. Res., 2022)

Change point localization in dependent dynamic nonparametric random dot product graphs. - Oscar Hernan Madrid Padilla; Yi Yu; Carey E. Priebe (J. Mach. Learn. Res., 2022)

Optimal Nonparametric Multivariate Change Point Detection and Localization. - Oscar Hernan Madrid Padilla; Yi Yu; Daren Wang; Alessandro Rinaldo (IEEE Trans. Inf. Theory, 2022)

Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores. - Gabriel Ruiz; Oscar Hernan Madrid Padilla; Qing Zhou (Trans. Mach. Learn. Res., 2022)

Change point detection and inference in multivariate non-parametric models under mixing conditions. - Carlos Misael Madrid Padilla; Haotian Xu; Daren Wang; Oscar Hernan Madrid Padilla; Yi Yu (NeurIPS, 2023)

Bayesian Change Point Detection with Spike-and-Slab Priors. - Lorenzo Cappello; Oscar Hernan Madrid Padilla; Julia A. Palacios (J. Comput. Graph. Stat., 2023)

A partially separable model for dynamic valued networks. - Yik Lun Kei; Yanzhen Chen; Oscar Hernan Madrid Padilla (Comput. Stat. Data Anal., 2023)

kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection. - Marcos Matabuena; Juan Carlos Vidal; Oscar Hernan Madrid Padilla; Jukka-Pekka Onnela (CoRR, 2024)

Network two-sample test for block models. - Chung Kyong Nguen; Oscar Hernan Madrid Padilla; Arash A. Amini (CoRR, 2024)

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory. - Zhi Zhang 012; Chris Chow; Yasi Zhang; Yanchao Sun; Haochen Zhang; Eric Hanchen Jiang; Han Liu 001; Furong Huang; Yuchen Cui; Oscar Hernan Madrid Padilla (CoRR, 2024)

Dense ReLU Neural Networks for Temporal-spatial Model. - Zhi Zhang 012; Carlos Misael Madrid Padilla; Xiaokai Luo; Oscar Hernan Madrid Padilla; Daren Wang (CoRR, 2024)

Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks. - Carlos Misael Madrid Padilla; Oscar Hernan Madrid Padilla; Yik Lun Kei; Zhi Zhang 012; Yanzhen Chen (CoRR, 2024)

Variance estimation in graphs with the fused lasso. - Oscar Hernan Madrid Padilla (J. Mach. Learn. Res., 2024)

Network Online Change Point Localization. - Yi Yu; Oscar Hernan Madrid Padilla; Daren Wang; Alessandro Rinaldo (SIAM J. Math. Data Sci., 2024)

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory. - Zhi Zhang 012; Chris Chow; Yasi Zhang; Yanchao Sun; Haochen Zhang; Eric Hanchen Jiang; Han Liu 001; Furong Huang; Yuchen Cui; Oscar Hernan Madrid Padilla (AISTATS, 2025)

Quantile Additive Trend Filtering. - Zhi Zhang 012; Kyle Ritscher; Oscar Hernan Madrid Padilla (AISTATS, 2025)

Change Point Detection on A Separable Model for Dynamic Networks. - Yik Lun Kei; Hangjian Li; Yanzhen Chen; Oscar Hernan Madrid Padilla (Trans. Mach. Learn. Res., 2025)

Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model. - Yik Lun Kei; Jialiang Li; Hangjian Li; Yanzhen Chen; Oscar Hernan Madrid Padilla (Trans. Mach. Learn. Res., 2025)

Risk Bounds For Distributional Regression. - Carlos Misael Madrid Padilla; Oscar Hernan Madrid Padilla; Sabyasachi Chatterjee (CoRR, 2025)

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces. - Marcos Matabuena; Rahul Ghosal; Pavlo Mozharovskyi; Oscar Hernan Madrid Padilla; Jukka-Pekka Onnela (CoRR, 2025)

Bayesian Variance Change Point Detection With Credible Sets. - Lorenzo Cappello; Oscar Hernan Madrid Padilla (IEEE Trans. Pattern Anal. Mach. Intell., 2025)