13 papers
Gaussian-efficient testing by betting on the mean of bounded data
Diego Martinez-Taboada, Aaditya Ramdas
Given -valued random variables such that for all , we propose a new nonasymptotic confidence interval for tha…
Bentkus-type asymptotic e-values
Diego Martinez-Taboada, Ben Chugg, Aaditya Ramdas
Asymptotic e-values are emerging as a powerful alternative to asymptotic p-values, particularly in post-hoc inference and multiple testing, where significance levels may be data-de…
Intrinsic-dimension empirical Bernstein inequalities for bounded self-adjoint operators
Diego Martinez-Taboada, Aaditya Ramdas
Operator-valued concentration inequalities are foundational to the analysis of modern high-dimensional statistics and randomized algorithms. However, standard oracle bounds are fre…
Intrinsic dimension concentration inequalities for self-adjoint operators
Diego Martinez-Taboada, Aaditya Ramdas
We derive novel concentration inequalities for the operator norm of the sum of self-adjoint operators that do not explicitly depend on the underlying dimension of the operator, but…
Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity
Diego Martinez-Taboada, Tomas Gonzalez, Aaditya Ramdas
The study of self-normalized processes plays a crucial role in a wide range of applications, from sequential decision-making to econometrics. While the behavior of self-normalized…
Sharp Empirical Bernstein Bounds for the Variance of Bounded Random Variables
Diego Martinez-Taboada, Aaditya Ramdas
We develop novel empirical Bernstein inequalities for the variance of bounded random variables. Our inequalities hold under constant conditional variance and mean, without further…
Nonasymptotic Heavy-Tailed Mean Estimation in Smooth Banach Spaces
Justin Whitehouse, Ben Chugg, Diego Martinez-Taboada +1
We consider the problem of nonasymptotic mean estimation for heavy-tailed random variables taking values in a smooth Banach space. In particular, we extend and improve a simple tru…
Sequential Kernelized Stein Discrepancy
Diego Martinez-Taboada, Aaditya Ramdas
We present a sequential version of the kernelized Stein discrepancy goodness-of-fit test, which allows for conducting goodness-of-fit tests for unnormalized densities that are cont…
Empirical Bernstein in smooth Banach spaces
Diego Martinez-Taboada, Aaditya Ramdas
Existing concentration bounds for bounded vector-valued random variables include extensions of the scalar Hoeffding and Bernstein inequalities. While the latter is typically tighte…
Counterfactual Density Estimation using Kernel Stein Discrepancies
Diego Martinez-Taboada, Edward H. Kennedy
Causal effects are usually studied in terms of the means of counterfactual distributions, which may be insufficient in many scenarios. Given a class of densities known up to normal…
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Diego Martinez-Taboada, Aaditya Ramdas, Edward H. Kennedy
The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments.…
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation
Diego Martinez-Taboada, Dino Sejdinovic
The counterfactual distribution models the effect of the treatment in the untreated group. While most of the work focuses on the expected values of the treatment effect, one may be…
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings
Diego Martinez-Taboada, Dino Sejdinovic
The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. I…