arXiv research

Paper improves confidence set construction for SGD using multiplier bootstrap.

Paper improves confidence intervals for LSA with multiplier bootstrap.

Paper develops Gaussian approximations and bootstrap for federated LSA with trade-off bounds.

Gaussian and bootstrap methods improve ATE estimator accuracy.

The paper provides Gaussian approximations for decentralized Federated Learning.

SIREN protocol corrects optimistic winner's scores in LLM evaluation.

Paper improves CLT and bootstrap approximations for LSA with decreasing step size.

New bootstraps improve speed and accuracy for graph count functionals.

Paper derives convergence rates and confidence intervals for LSA with Markovian noise.

New framework assesses LLMs' expertise using nonparametric ranking and confidence diagrams.

Upper Confidence Bound (UCB) method is arguably the most celebrated one used in online decision making with partial information feedback. Existing techniques for constructing confidence bounds are typically built upon various concentration inequalities, which thus lead to over-exploration. In this paper, we propose a n…

Paper develops methods for statistical inference with SGD in nonconvex optimization.

In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we bootstrap on the master machine without over-resampling, typically required by existing methods \cite{kleiner2014scalable,sengupta2016subsam…

This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample s…

Estimates causal effects using machine learning for binary treatment and mediator.

Develops statistical confidence sets for multidimensional scaling.

Develops methods for estimating and providing confidence bands in sparse high-dimensional additive models.

Proposes a private empirical bootstrap for Gaussian Differential Privacy.

We quantify uncertainty in Oja's algorithm's leading eigenvector estimation.

This paper proposes a unified framework to quantify local and global inferential uncertainty for high dimensional nonparanormal graphical models. In particular, we consider the problems of testing the presence of a single edge and constructing a uniform confidence subgraph. Due to the presence of unknown marginal trans…

Researchers use Gaussian processes to approximate Lagrange multipliers for Maximum-Entropy distributions.

Bagging stabilizes linear interpolators, improving their generalization performance.

Kernel ridge regression inference for nonstandard data.

We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and establish a near-linear lower bound on the regret incurred by it under the bandit model with Bernoulli rewards. We show that NPB with an approp…

Data augmented bootstrap unifies various confidence interval construction methods.

Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover such causal models and has been extended in various directions. An important problem with LiNGAM is that the results a…

New DP bootstrap method for statistical inference with improved privacy and accuracy.

New method for estimating high-dimensional binary time series coefficients.

Automates debiasing for large language model evaluations through Fisher random walk.

Unified method for inference on partially identified causal effects using covariates.

New method for inference on covariates in NMF with random effects.

BYOV combines SSL and Bayesian methods for uncertainty estimation.

The paper provides CI for test unfairness of group-fairness-aware classifiers trained with online SGD.

New algorithm for estimating MLR parameters with non-Gaussian noise.

Boosts kernel two-sample test power with multiple kernels.

BNEM improves Boltzmann sampler efficiency.

The paper develops methods to infer membership probabilities and rank network nodes using the DCMM model.

The paper develops methods for causal inference from single-cell RNA sequencing data with multiple outcomes.

Ridge regression is revisited with debiasing and thresholding, offering advantages over Lasso.

SDRF estimates complex survey designs for conditional distributions.

The paper ranks items based on top choices in multiway comparisons.

Optimizes a small set of centroid points to approximate bootstrap distribution.

Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling based on Markov chain Monte Carlo can be too computationally intensive, is serial in nature and mixes poorly between posterior modes. Further, all models are misspecified, which brings into question the val…

Gaussian process is a very promising novel technology that has been applied to both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification problem, because we encounter intractable integrals. In this paper we …

In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoot}). The \texttt{ReBoot} enforces exploration by injecting data-driven randomness through a residual-based perturbation mechanism. This nove…

AR-Sieve Bootstrap improves Random Forest time series prediction accuracy.

We consider the performance of the bootstrap in high-dimensions for the setting of linear regression, where $p<n$ but $p/n$ is not close to zero. We consider ordinary least-squares as well as robust regression methods and adopt a minimalist performance requirement: can the bootstrap give us good confidence intervals fo…

Cryptocurrency markets treat infrastructure failures and regulatory shocks differently, but the effect is not statistically significant.