2 citations · 5 across the 8 of their papers we have counts for
9 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…
MoMoE: A Mixture of Expert Agent Model for Financial Sentiment Analysis
Peng Shu, Junhao Chen, Zhengliang Liu +8
We present a novel approach called Mixture of Mixture of Expert (MoMoE) that combines the strengths of Mixture-of-Experts (MoE) architectures with collaborative multi-agent framewo…
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…
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,…
Scalable inference in functional linear regression with streaming data
Jinhan Xie, Enze Shi, Peijun Sang +3
Traditional static functional data analysis is facing new challenges due to streaming data, where data constantly flow in. A major challenge is that storing such an ever-increasing…
An adaptive model checking test for functional linear model
Enze Shi, Yi Liu, Ke Sun +2
Numerous studies have been devoted to the estimation and inference problems for functional linear models (FLM). However, few works focus on model checking problem that ensures the…
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi +4
The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…