Search Results for author:
Found 17 papers, 6 papers with code
A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules
In particular, we derive optimal detection rules for these watermarks under our framework.
MISNN: Multiple Imputation via Semi-parametric Neural Networks
Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream data analysis.
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data
Single imputation methods such as matrix completion methods do not adequately account for imputation uncertainty and hence would yield improper statistical inference.
CEDAR: Communication Efficient Distributed Analysis for Regressions
We provide theoretical investigation for the asymptotic properties of the proposed method for statistical inference as well as differential privacy, and evaluate its performance in simulations and real data analyses in comparison with several recently developed methods.
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation
Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require correct specification of the propensity score model.
Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis.
Assessing Fairness in the Presence of Missing Data
When the goal is to develop a fair algorithm in the complete data domain where there are no missing values, an algorithm that is fair in the complete case domain may show disproportionate bias towards some marginalized groups in the complete data domain.
Fairness in Missing Data Imputation
Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings.
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability
Interestingly, we show a new equivalence between DP-SGD and DP-SGLD, implying that some non-Bayesian DP training naturally allows for uncertainty quantification.
On the Convergence and Calibration of Deep Learning with Differential Privacy
Differentially private (DP) training preserves the data privacy usually at the cost of slower convergence (and thus lower accuracy), as well as more severe mis-calibration than its non-private counterpart.
A Theorem of the Alternative for Personalized Federated Learning
A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often come from different but not entirely unrelated distributions, and personalization is, therefore, necessary to achieve optimal results from each individual's perspective.
Federated $f$-Differential Privacy
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data.
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
More importantly, when moving to the imbalanced case, our analysis of the Layer-Peeled Model reveals a hitherto unknown phenomenon that we term \textit{Minority Collapse}, which fundamentally limits the performance of deep learning models on the minority classes.
Fairness guarantee in analysis of incomplete data
While there is a growing body of literature on fairness in analysis of fully observed data, there has been little work on investigating fairness in analysis of incomplete data when the goal is to develop a fair algorithm in the complete data domain where there are no missing values.
Grouping effects of sparse CCA models in variable selection
The sparse canonical correlation analysis (SCCA) is a bi-multivariate association model that finds sparse linear combinations of two sets of variables that are maximally correlated with each other.
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
To address this question, we introduce a family of analytical and sharp privacy bounds under composition using the Edgeworth expansion in the framework of the recently proposed f-differential privacy.
Deep Learning with Gaussian Differential Privacy
Leveraging the appealing properties of $f$-differential privacy in handling composition and subsampling, this paper derives analytically tractable expressions for the privacy guarantees of both stochastic gradient descent and Adam used in training deep neural networks, without the need of developing sophisticated techniques as [3] did.
