Edgar Dobriban

research

∙ 08/03/2023

Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory

Distribution shifts are a serious concern in modern statistical learning...

0 Patrick Chao, et al. ∙

research

∙ 07/20/2023

A Framework for Statistical Inference via Randomized Algorithms

Randomized algorithms, such as randomized sketching or projections, are ...

0 Zhixiang Zhang, et al. ∙

research

∙ 06/28/2023

Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift

Statistical machine learning methods often face the challenge of limited...

0 Hongxiang Qiu, et al. ∙

research

∙ 04/18/2023

Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning

We propose a new method for high-dimensional semi-supervised learning pr...

0 Tengyao Wang, et al. ∙

research

∙ 03/01/2023

Joint Coverage Regions: Simultaneous Confidence and Prediction Sets

We introduce Joint Coverage Regions (JCRs), which unify confidence inter...

0 Edgar Dobriban, et al. ∙

research

∙ 11/09/2022

Conformal Frequency Estimation with Sketched Data under Relaxed Exchangeability

A flexible method is developed to construct a confidence interval for th...

0 Matteo Sesia, et al. ∙

research

∙ 10/05/2022

Doubly Robust Proximal Synthetic Controls

To infer the treatment effect for a single treated unit using panel data...

0 Hongxiang Qiu, et al. ∙

research

∙ 07/06/2022

PAC Prediction Sets for Meta-Learning

Uncertainty quantification is a key component of machine learning models...

0 Sangdon Park, et al. ∙

research

∙ 06/18/2022

Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games

We consider the problem of learning discriminative representations for d...

0 Druv Pai, et al. ∙

research

∙ 06/16/2022

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

We introduce a unified framework for group equivariant networks on homog...

15 Yinshuang Xu, et al. ∙

research

∙ 06/10/2022

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

The performance of machine learning models can significantly degrade und...

0 Souradeep Dutta, et al. ∙

research

∙ 05/22/2022

PAC-Wrap: Semi-Supervised PAC Anomaly Detection

Anomaly detection is essential for preventing hazardous outcomes for saf...

0 Shuo Li, et al. ∙

research

∙ 05/15/2022

Fair Bayes-Optimal Classifiers Under Predictive Parity

Increasing concerns about disparate effects of AI have motivated a great...

0 Xianli Zeng, et al. ∙

research

∙ 04/05/2022

SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space

We propose the first SE(3)-equivariant coordinate-based network for lear...

0 Evangelos Chatzipantazis, et al. ∙

research

∙ 03/11/2022

Distribution-free Prediction Sets Adaptive to Unknown Covariate Shift

Predicting sets of outcomes – instead of unique outcomes – is a promisin...

0 Hongxiang Qiu, et al. ∙

research

∙ 02/25/2022

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

Environments with sparse rewards and long horizons pose a significant ch...

9 Souradeep Dutta, et al. ∙

research

∙ 02/20/2022

Bayes-Optimal Classifiers under Group Fairness

Machine learning algorithms are becoming integrated into more and more h...

0 Xianli Zeng, et al. ∙

research

∙ 01/07/2022

iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection

Machine learning methods such as deep neural networks (DNNs), despite th...

0 Ramneet Kaur, et al. ∙

research

∙ 11/16/2021

Learning Augmentation Distributions using Transformed Risk Minimization

Adapting to the structure of data distributions (such as symmetry and tr...

0 Evangelos Chatzipantazis, et al. ∙

research

∙ 10/04/2021

Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients

There has been a growing need to provide Byzantine-resilience in distrib...

3 Lingjiao Chen, et al. ∙

research

∙ 08/26/2021

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

Modern methods for learning from data depend on many tuning parameters, ...

0 Dominic Richards, et al. ∙

research

∙ 06/17/2021

PAC Prediction Sets Under Covariate Shift

An important challenge facing modern machine learning is how to rigorous...

0 Sangdon Park, et al. ∙

research

∙ 04/25/2021

Consistency of invariance-based randomization tests

Invariance-based randomization tests – such as permutation tests – are a...

0 Edgar Dobriban, et al. ∙

research

∙ 03/17/2021

Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition

Adversarially trained models exhibit a large generalization gap: they ca...

8 Yaodong Yu, et al. ∙

research

∙ 12/05/2020

Selecting the number of components in PCA via random signflips

Dimensionality reduction via PCA and factor analysis is an important too...

0 David Hong, et al. ∙

research

∙ 11/21/2020

Sparse sketches with small inversion bias

For a tall n× d matrix A and a random m× n sketching matrix S, the sketc...

0 Michał Dereziński, et al. ∙

research

∙ 10/11/2020

What causes the test error? Going beyond bias-variance via ANOVA

Modern machine learning methods are often overparametrized, allowing ada...

11 Licong Lin, et al. ∙

research

∙ 06/26/2020

DeltaGrad: Rapid retraining of machine learning models

Machine learning models are not static and may need to be retrained on s...

0 Yinjun Wu, et al. ∙

research

∙ 05/01/2020

How to reduce dimension with PCA and random projections?

In our "big data" age, the size and complexity of data is steadily incre...

0 Fan Yang, et al. ∙

research

∙ 03/17/2020

The Implicit Regularization of Stochastic Gradient Flow for Least Squares

We study the implicit regularization of mini-batch stochastic gradient d...

8 Alnur Ali, et al. ∙

research

∙ 02/03/2020

Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods

We provide an exact analysis of the limiting spectrum of matrices random...

0 Jonathan Lacotte, et al. ∙

research

∙ 11/18/2019

Implicit Regularization of Normalization Methods

Normalization methods such as batch normalization are commonly used in o...

17 Xiaoxia Wu, et al. ∙

research

∙ 10/06/2019

Ridge Regression: Structure, Cross-Validation, and Sketching

We study the following three fundamental problems about ridge regression...

0 Sifan Liu, et al. ∙

research

∙ 07/25/2019

Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond

Many complex deep learning models have found success by exploiting symme...

0 Shuxiao Chen, et al. ∙

research

∙ 03/22/2019

One-shot distributed ridge regression in high dimensions

In many areas, practitioners need to analyze large datasets that challen...

0 Edgar Dobriban, et al. ∙

research

∙ 10/14/2018

A New Theory for Sketching in Linear Regression

Large datasets create opportunities as well as analytic challenges. A re...

0 Edgar Dobriban, et al. ∙

research

∙ 09/30/2018

Distributed linear regression by averaging

Modern massive datasets pose an enormous computational burden to practit...

0 Edgar Dobriban, et al. ∙

research

∙ 07/01/2018

Robust Inference Under Heteroskedasticity via the Hadamard Estimator

Drawing statistical inferences from large datasets in a model-robust way...

0 Edgar Dobriban, et al. ∙

research

∙ 06/26/2018

Flexible Multiple Testing with the FACT Algorithm

Modern high-throughput science often leads to multiple testing problems:...

0 Edgar Dobriban, et al. ∙

research

∙ 11/11/2017

Deterministic parallel analysis

Factor analysis is widely used in many application areas. The first step...

0 Edgar Dobriban, et al. ∙

Edgar Dobriban

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