3 citations · 3 across the 10 of their papers we have counts for
12 papers
Online Augmented Plug-in Inference for Multi-group Fair Prediction
Enze Shi, Jinhan Xie, Linglong Kong +1
Fairness regularization changes both a prediction rule and its sampling uncertainty. We develop online inference for classifiers trained with a covariance penalty on group mean dec…
Differentially Private Conformal Prediction
Jiamei Wu, Ce Zhang, Zhipeng Cai +4
Conformal prediction (CP) has attracted broad attention as a simple and flexible framework for uncertainty quantification through prediction sets. In this work, we study how to dep…
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…
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…
Fast Online Elastic Net Subspace Clustering via A Novel Dictionary Update Strategy
Wentao Qu, Lingchen Kong, Linglong Kong +1
With the rapid growth of data volume and the increasing demand for real-time analysis, online subspace clustering has emerged as an effective tool for processing dynamic data strea…
Predicting Market Trends with Enhanced Technical Indicator Integration and Classification Models
Abdelatif Hafid, Abderazzak Mouiha, Linglong Kong +4
Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial…