22 citations · 31 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
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…
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks
Enze Shi, Linglong Kong, Bei Jiang
Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Lea…
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…
Oblivious subspace embeddings for compressed Tucker decompositions
Matthew Pietrosanu, Bei Jiang, Linglong Kong
Emphasis in the tensor literature on random embeddings (tools for low-distortion dimension reduction) for the canonical polyadic (CP) tensor decomposition has left analogous result…