E. Dobriban

发表

High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

Stefan Wager, E. Dobriban, Edgar Dobriban, 2015, 1507.03003.

A Group-Theoretic Framework for Data Augmentation

Edgar Dobriban, Shuxiao Chen, Jane H Lee, 2019, NeurIPS.

$e$PCA: High dimensional exponential family PCA

Lydia T. Liu, A. Singer, E. Dobriban, 2016, The Annals of Applied Statistics.

Sharp detection in PCA under correlations: all eigenvalues matter

E. Dobriban, Edgar Dobriban, 2016, 1602.06896.

Permutation methods for factor analysis and PCA

Edgar Dobriban, E. Dobriban, Edgar Dobriban, 2017, 1710.00479.

Implicit Regularization and Convergence for Weight Normalization

Suriya Gunasekar, Rachel A. Ward, Qiang Liu, 2019, Neural Information Processing Systems.

Submitted to the Annals of Statistics OPTIMAL PREDICTION IN THE LINEARLY TRANSFORMED SPIKED MODEL By

A. Singer, W. Leeb, E. Dobriban, 2019 .

PCA from noisy, linearly reduced data: the diagonal case

A. Singer, W. Leeb, E. Dobriban, 2016, 1611.10333.

Distributed linear regression by averaging

E. Dobriban, Yueqi Sheng, Edgar Dobriban, 2018, The Annals of Statistics.

Selecting the number of components in PCA via random signflips.

E. Dobriban, Yueqi Sheng, D. Hong, 2020, 2012.02985.

Deterministic parallel analysis: An improved method for selecting the number of factors and principal components

A. Owen, E. Dobriban, Edgar Dobriban, 2017 .

Optimal prediction in the linearly transformed spiked model

Amit Singer, Edgar Dobriban, William Leeb, 2017, The Annals of Statistics.

Genome-Wide Scan Informed by Age-Related Disease Identifies Loci for Exceptional Human Longevity

A. Owen, Stuart K. Kim, D. Mari, 2015, PLoS genetics.

Asymptotics for Sketching in Least Squares

E. Dobriban, Edgar Dobriban, 2019 .

One-shot distributed ridge regression in high dimensions

Edgar Dobriban, Yue Sheng, E. Dobriban, 2019, J. Mach. Learn. Res..

Robust Inference Under Heteroskedasticity via the Hadamard Estimator

Weijie J. Su, E. Dobriban, Edgar Dobriban, 2018, 1807.00347.

Distribution-free Prediction Sets Adaptive to Unknown Covariate Shift

E. Dobriban, Hongxiang Qiu, E. T. Tchetgen, 2022, 2203.06126.

Memory Classifiers: Two-stage Classification for Robustness in Machine Learning

I. Lee, E. Dobriban, E. Bernardis, 2022, ArXiv.

Bayes-Optimal Classifiers under Group Fairness

E. Dobriban, Xianli Zeng, Guang Cheng, 2022, ArXiv.

Fair Bayes-Optimal Classifiers Under Predictive Parity

E. Dobriban, Xianli Zeng, Guang Cheng, 2022, NeurIPS.

Collaborative Learning of Distributions under Heterogeneity and Communication Constraints

Hamed Hassani, E. Dobriban, Dong-hwan Lee, 2022, ArXiv.

Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games

E. Dobriban, Y. Ma, Druv Pai, 2022, ArXiv.

Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates

Osbert Bastani, I. Lee, James Weimer, 2022, ArXiv.

PAC Prediction Sets for Meta-Learning

Osbert Bastani, I. Lee, E. Dobriban, 2022, 2207.02440.

Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?

E. Dobriban, Dominic Richards, Patrick Rebeschini, 2021, ArXiv.

PAC-Wrap: Semi-Supervised PAC Anomaly Detection

I. Lee, O. Sokolsky, E. Dobriban, 2022, KDD.

Distribution-Free Prediction Sets Adaptive to Unknown Covariate Shift

E. Dobriban, Hongxiang Qiu, E. T. Tchetgen, 2022 .

Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games

E. Dobriban, Y. Ma, Druv Pai, 2022, ArXiv.

Memory Classifiers: Two-stage Classification for Robustness in Machine Learning

I. Lee, E. Dobriban, E. Bernardis, 2022, ArXiv.

Implicit Regularization and Convergence for Weight Normalization

Suriya Gunasekar, Rachel A. Ward, Qiang Liu, 2019, NeurIPS.

Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints

Hamed Hassani, E. Dobriban, Dong-hwan Lee, 2022, NeurIPS.

Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

Kostas Daniilidis, E. Dobriban, Yinshuang Xu, 2022, ICML.

PAC Prediction Sets for Meta-Learning

I. Lee, E. Dobriban, Sangdon Park, 2022, NeurIPS.

Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games

E. Dobriban, Y. Ma, Druv Pai, 2022, J. Frankl. Inst..

Doubly Robust Proximal Synthetic Controls

E. Dobriban, W. Miao, Hongxiang Qiu, 2022, 2210.02014.