22 citations · 34 across the 25 of their papers we have counts for
10 papers · 1 filter
CBMA: Improving conformal prediction through Bayesian model averaging
Pankaj Bhagwat, Linglong Kong, Bei Jiang
Conformal prediction has emerged as a popular technique for facilitating valid predictive inference across a spectrum of machine learning models, under minimal assumption of exchan…
Conformal Inference For Missing Data under Multiple Robust Learning
Wenlu Tang, Hongni Wang, Xingcai Zhou +2
We develop a novel approach to tackle the common but challenging problem of conformal inference for missing data in machine learning, focusing on Missing at Random (MAR) data. We p…
Budget-constrained Active Learning to Effectively De-censor Survival Data
Ali Parsaee, Bei Jiang, Zachary Friggstad +1
Standard supervised learners attempt to learn a model from a labeled dataset. Given a small set of labeled instances, and a pool of unlabeled instances, a budgeted learner can use…
Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis
Enze Shi, Pankaj Bhagwat, Zhixian Yang +2
Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impos…
Toward Optimal Statistical Inference in Noisy Linear Quadratic Reinforcement Learning over a Finite Horizon
Bo Pan, Jianya Lu, Yafei Wang +3
Recent developments in Reinforcement learning have significantly enhanced sequential decision-making in uncertain environments. Despite their strong performance guarantees, most ex…
Non-Asymptotic Analysis of Online Local Private Learning with SGD
Enze Shi, Jinhan Xie, Bei Jiang +2
Differentially Private Stochastic Gradient Descent (DP-SGD) has been widely used for solving optimization problems with privacy guarantees in machine learning and statistics. Despi…
Online differentially private inference in stochastic gradient descent
Jinhan Xie, Enze Shi, Bei Jiang +2
We propose a general privacy-preserving optimization-based framework for real-time environments without requiring trusted data curators. In particular, we introduce a noisy stochas…
Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization
Enze Shi, Linglong Kong, Yiqun Xiao +1
Ensuring fairness is essential as machine learning increasingly informs consequential decisions. However, many fairness-aware methods focus on the outputs of individual predictors,…
Online federated learning framework for classification
Wenxing Guo, Jinhan Xie, Jianya Lu +3
In this paper, we develop a novel online federated learning framework for classification, designed to handle streaming data from multiple clients while ensuring data privacy and co…
A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation
Forough Fazeliasl, Michael Minyi Zhang, Bei Jiang +1
Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, imp…