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09/07/2023
Total Variation Floodgate for Variable Importance Inference in Classification
Inferring variable importance is the key problem of many scientific stud...
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04/23/2023
Policy Learning under Biased Sample Selection
Practitioners often use data from a randomized controlled trial to learn...
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12/29/2022
What Estimators Are Unbiased For Linear Models?
The recent thought-provoking paper by Hansen [2022, Econometrica] proved...
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09/05/2022
Learning from a Biased Sample
The empirical risk minimization approach to data-driven decision making ...
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08/19/2022
Improving knockoffs with conditional calibration
The knockoff filter of Barber and Candes (arXiv:1404.5609) is a flexible...
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08/13/2022
Machine learning meets false discovery rate
Classical false discovery rate (FDR) controlling procedures offer strong...
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10/03/2021
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce Learn then Test, a framework for calibrating machine learni...
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07/29/2021
Double-Robust Two-Way-Fixed-Effects Regression For Panel Data
We propose a new estimator for the average causal effects of a binary tr...
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06/29/2021
BONuS: Multiple multivariate testing with a data-adaptivetest statistic
We propose a new adaptive empirical Bayes framework, the Bag-Of-Null-Sta...
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04/16/2021
Testing for Outliers with Conformal p-values
This paper studies the construction of p-values for nonparametric outlie...
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03/17/2021
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling a...
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01/07/2021
Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learni...
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07/20/2020
Conditional calibration for false discovery rate control under dependence
We introduce a new class of methods for finite-sample false discovery ra...
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06/11/2020
Conformal Inference of Counterfactuals and Individual Treatment Effects
Evaluating treatment effect heterogeneity widely informs treatment decis...
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04/30/2020
Consistency of Spectral Clustering on Hierarchical Stochastic Block Models
We propose a generic network model, based on the Stochastic Block Model,...
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02/13/2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity is an important yet under-studied property in modern optimiza...
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01/27/2020
Variance Reduction with Sparse Gradients
Variance reduction methods such as SVRG and SpiderBoost use a mixture of...
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11/20/2019
Smoothed Nested Testing on Directed Acyclic Graphs
We consider the problem of multiple hypothesis testing when there is a l...
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09/11/2019
Unified ℓ_2→∞ Eigenspace Perturbation Theory for Symmetric Random Matrices
Modern applications in statistics, computer science and network science ...
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07/13/2019
An Assumption-Free Exact Test For Fixed-Design Linear Models With Exchangeable Errors
We propose the cyclic permutation test (CPT) to test general linear hypo...
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04/09/2019
On the Adaptivity of Stochastic Gradient-Based Optimization
Stochastic-gradient-based optimization has been a core enabling methodol...
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06/20/2018
Regression adjustment in randomized experiments with a diverging number of covariates
Extending R. A. Fisher and D. A. Freedman's results on the analysis of c...
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11/07/2017
Overlap in Observational Studies with High-Dimensional Covariates
Causal inference in observational settings typically rests on a pair of ...
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09/12/2016