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08/03/2023
Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory
Distribution shifts are a serious concern in modern statistical learning...
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07/20/2023
A Framework for Statistical Inference via Randomized Algorithms
Randomized algorithms, such as randomized sketching or projections, are ...
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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...
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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...
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03/01/2023
Joint Coverage Regions: Simultaneous Confidence and Prediction Sets
We introduce Joint Coverage Regions (JCRs), which unify confidence inter...
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11/09/2022
Conformal Frequency Estimation with Sketched Data under Relaxed Exchangeability
A flexible method is developed to construct a confidence interval for th...
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10/05/2022
Doubly Robust Proximal Synthetic Controls
To infer the treatment effect for a single treated unit using panel data...
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07/06/2022
PAC Prediction Sets for Meta-Learning
Uncertainty quantification is a key component of machine learning models...
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06/18/2022
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games
We consider the problem of learning discriminative representations for d...
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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...
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06/10/2022
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning
The performance of machine learning models can significantly degrade und...
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05/22/2022
PAC-Wrap: Semi-Supervised PAC Anomaly Detection
Anomaly detection is essential for preventing hazardous outcomes for saf...
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05/15/2022
Fair Bayes-Optimal Classifiers Under Predictive Parity
Increasing concerns about disparate effects of AI have motivated a great...
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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...
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03/11/2022
Distribution-free Prediction Sets Adaptive to Unknown Covariate Shift
Predicting sets of outcomes – instead of unique outcomes – is a promisin...
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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...
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02/20/2022
Bayes-Optimal Classifiers under Group Fairness
Machine learning algorithms are becoming integrated into more and more h...
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01/07/2022
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Machine learning methods such as deep neural networks (DNNs), despite th...
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11/16/2021
Learning Augmentation Distributions using Transformed Risk Minimization
Adapting to the structure of data distributions (such as symmetry and tr...
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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...
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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, ...
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06/17/2021
PAC Prediction Sets Under Covariate Shift
An important challenge facing modern machine learning is how to rigorous...
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04/25/2021
Consistency of invariance-based randomization tests
Invariance-based randomization tests – such as permutation tests – are a...
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03/17/2021
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition
Adversarially trained models exhibit a large generalization gap: they ca...
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12/05/2020
Selecting the number of components in PCA via random signflips
Dimensionality reduction via PCA and factor analysis is an important too...
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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...
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10/11/2020
What causes the test error? Going beyond bias-variance via ANOVA
Modern machine learning methods are often overparametrized, allowing ada...
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06/26/2020
DeltaGrad: Rapid retraining of machine learning models
Machine learning models are not static and may need to be retrained on s...
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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...
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03/17/2020
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
We study the implicit regularization of mini-batch stochastic gradient d...
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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...
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11/18/2019
Implicit Regularization of Normalization Methods
Normalization methods such as batch normalization are commonly used in o...
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10/06/2019
Ridge Regression: Structure, Cross-Validation, and Sketching
We study the following three fundamental problems about ridge regression...
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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...
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03/22/2019
One-shot distributed ridge regression in high dimensions
In many areas, practitioners need to analyze large datasets that challen...
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10/14/2018
A New Theory for Sketching in Linear Regression
Large datasets create opportunities as well as analytic challenges. A re...
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09/30/2018
Distributed linear regression by averaging
Modern massive datasets pose an enormous computational burden to practit...
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07/01/2018
Robust Inference Under Heteroskedasticity via the Hadamard Estimator
Drawing statistical inferences from large datasets in a model-robust way...
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06/26/2018
Flexible Multiple Testing with the FACT Algorithm
Modern high-throughput science often leads to multiple testing problems:...
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11/11/2017