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Found 12 papers, 2 papers with code
Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators
In many instances, an analyst justifies her claim by imposing complexity-reducing assumptions on $b$ and $p$ to ensure "rate double-robustness".
On Undersmoothing and Sample Splitting for Estimating a Doubly Robust Functional
We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature.
Sparse Signal Detection in Heteroscedastic Gaussian Sequence Models: Sharp Minimax Rates
Given a heterogeneous Gaussian sequence model with unknown mean $\theta \in \mathbb R^d$ and known covariance matrix $\Sigma = \operatorname{diag}(\sigma_1^2,\dots, \sigma_d^2)$, we study the signal detection problem against sparse alternatives, for known sparsity $s$.
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond
In recent times, inference for the ATE in the presence of high-dimensional covariates has been extensively studied.
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
We develop a simple and unified framework for nonlinear variable selection that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (e. g., tree ensembles, kernel methods, neural networks, etc).
On the Existence of Universal Lottery Tickets
The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation.
Cross-Cluster Weighted Forests
Adapting machine learning algorithms to better handle clustering or batch effects within training data sets is important across a wide variety of biological applications.
Detecting Structured Signals in Ising Models
In this paper, we study the effect of dependence on detecting a class of signals in Ising models, where the signals are present in a structured way.
Probability Statistics Theory Statistics Theory 62G10, 62G20, 62C20
Semi-Supervised Off Policy Reinforcement Learning
2) The surrogate variables we leverage in the modified SSL framework are predictive of the outcome but not informative to the optimal policy or value function.
Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
This is the rejoinder to the discussion by Kennedy, Balakrishnan and Wasserman on the paper "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning" published in Statistical Science.
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
In this paper, we introduce essentially assumption-free tests that (i) can falsify the null hypothesis that the bias of $\hat{\psi}_{1}$ is of smaller order than its standard error, (ii) can provide an upper confidence bound on the true coverage of the Wald interval, and (iii) are valid under the null under no smoothness/sparsity assumptions on the nuisance parameters.
On Estimation of $L_{r}$-Norms in Gaussian White Noise Models
We provide a complete picture of asymptotically minimax estimation of $L_r$-norms (for any $r\ge 1$) of the mean in Gaussian white noise model over Nikolskii-Besov spaces.
