22 citations · 34 across the 25 of their papers we have counts for
26 papers
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…
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…
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…
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Tianyang Zhong, Zhengliang Liu, Yi Pan +74
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, includi…
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…
Gaussian Differential Privacy on Riemannian Manifolds
Yangdi Jiang, Xiaotian Chang, Yi Liu +3
We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds. The concept of GDP stands out as a prominent privacy definition t…
High-dimensional outlier detection and variable selection via adaptive weighted mean regression
Jiaqi Li, Linglong Kong, Bei Jiang +1
This paper proposes an adaptive penalized weighted mean regression for outlier detection of high-dimensional data. In comparison to existing approaches based on the mean shift mode…
Online Local Differential Private Quantile Inference via Self-normalization
Yi Liu, Qirui Hu, Lei Ding +2
Based on binary inquiries, we developed an algorithm to estimate population quantiles under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides asymptotic…
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…
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
Yi Liu, Ke Sun, Linglong Kong +1
Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of sensitive individual information. Des…
Conformalized Fairness via Quantile Regression
Meichen Liu, Lei Ding, Dengdeng Yu +3
Algorithmic fairness has received increased attention in socially sensitive domains. While rich literature on mean fairness has been established, research on quantile fairness rema…
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?
Ke Sun, Bei Jiang, Linglong Kong
Distributional reinforcement learning, which focuses on learning the entire return distribution instead of only its expectation in standard RL, has demonstrated remarkable success…
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Ke Sun, Yingnan Zhao, Wulong Liu +2
The empirical success of distributional reinforcement learning (RL) highly relies on the choice of distribution divergence equipped with an appropriate distribution representation.…
Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability
Yafei Wang, Bo Pan, Wei Tu +6
Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with indepe…
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
Lei Ding, Dengdeng Yu, Jinhan Xie +7
With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have…