10 citations · 31 across the 22 of their papers we have counts for
23 papers
Towards a Statistical Understanding of Mixture-of-Experts
Siyuan He, Bokai Yang, Jie Hu +2
Mixture-of-experts (MoE) architectures increase model capacity by combining a collection of expert predictors through input-dependent routing, while often activating only a small s…
Local spectral clustering for heterogeneous clustering structures
Yuanxing Chen, Qingzhao Zhang, Yuhong Yang
Classical clustering methods typically assume that all informative features support a single latent partition of the observations. This assumption can be overly restrictive for mod…
Sign Hacking with Auxiliary Variable Exploration in the Age of Big Data
Jie Hu, Yuhong Yang
In linear regression, the signs of coefficients convey the direction of covariate effects and are central to empirical interpretation. In high-dimensional settings, however, the ab…
Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
Deqi Zheng, Xiaoyang Xu, Yuhong Yang
We study a stochastic multi-armed bandit problem in which the set of available arms expands over time. This setting arises in sequential experimentation when new actions or treatme…
Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects
Chongguang Tao, Zheng Zhou, Yuhong Yang
Uncertainty quantification for individual treatment effects (ITEs) is a daunting challenge in causal inference. Motivated by recent advances in conformal prediction, several works…
Conformal Prediction Assessment: A Framework for Conditional Coverage Evaluation and Selection
Zheng Zhou, Xiangfei Zhang, Chongguang Tao +1
Conformal prediction provides rigorous distribution-free finite-sample guarantees for marginal coverage under the assumption of exchangeability, but may exhibit systematic undercov…
Cross-Validation in Bipartite Networks
Bokai Yang, Yuanxing Chen, Yuhong Yang
Bipartite networks, which encode interactions between two distinct types of entities, arise widely in applications and exhibit inherent asymmetry across node sets. Despite a growin…
On damage of interpolation to adversarial robustness in regression
Jingfu Peng, Yuhong Yang
Deep neural networks (DNNs) typically involve a large number of parameters and are trained to achieve zero or near-zero training error. Despite such interpolation, they often exhib…
Adversarial learning for nonparametric regression: Minimax rate and adaptive estimation
Jingfu Peng, Yuhong Yang
Despite tremendous advancements of machine learning models and algorithms in various application domains, they are known to be vulnerable to subtle, natural or intentionally crafte…
Penalized Network Cross-Validation for Nested Models by Edge-Sampling
Bokai Yang, Yuanxing Chen, Yuhong Yang
In the network literature, a wide range of statistical models has been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a…
Network Model Averaging Prediction for Latent Space Models by K-Fold Edge Cross-Validation
Yan Zhang, Jun Liao, Xinyan Fan +2
In complex systems, networks represent connectivity relationships between nodes through edges. Latent space models are crucial in analyzing network data for tasks like community de…
Model Privacy: A Unified Framework for Understanding Model Stealing Attacks and Defenses
Ganghua Wang, Yuhong Yang, Jie Ding
The use of machine learning (ML) has become increasingly prevalent in various domains, highlighting the importance of understanding and ensuring its safety. One pressing concern is…
Golden Ratio-Based Sufficient Dimension Reduction
Wenjing Yang, Yuhong Yang
Many machine learning applications deal with high dimensional data. To make computations feasible and learning more efficient, it is often desirable to reduce the dimensionality of…
Minimax rates of convergence for nonparametric regression under adversarial attacks
Jingfu Peng, Yuhong Yang
Recent research shows the susceptibility of machine learning models to adversarial attacks, wherein minor but maliciously chosen perturbations of the input can significantly degrad…
Drift to Remember
Jin Du, Xinhe Zhang, Hao Shen +7
Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrop…
Additive-Effect Assisted Learning
Jiawei Zhang, Yuhong Yang, Jie Ding
It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modeling performance. We consider a sc…
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies
Mu-Huan Miles Chung, Sharon Li, Jaturong Kongmanee +7
Redacted emails satisfy most privacy requirements but they make it more difficult to detect anomalous emails that may be indicative of data exfiltration. In this paper we develop a…
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails
Mu-Huan Chung, Lu Wang, Sharon Li +6
Research on email anomaly detection has typically relied on specially prepared datasets that may not adequately reflect the type of data that occurs in industry settings. In our re…
Pruning Deep Neural Networks from a Sparsity Perspective
Enmao Diao, Ganghua Wang, Jiawei Zhan +3
In recent years, deep network pruning has attracted significant attention in order to enable the rapid deployment of AI into small devices with computation and memory constraints.…
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation
Wenjing Yang, Ganghua Wang, Jie Ding +1
The goal of model compression is to reduce the size of a large neural network while retaining a comparable performance. As a result, computation and memory costs in resource-limite…
Targeted Cross-Validation
Jiawei Zhang, Jie Ding, Yuhong Yang
In many applications, we have access to the complete dataset but are only interested in the prediction of a particular region of predictor variables. A standard approach is to find…
Is a Classification Procedure Good Enough? A Goodness-of-Fit Assessment Tool for Classification Learning
Jiawei Zhang, Jie Ding, Yuhong Yang
In recent years, many non-traditional classification methods, such as Random Forest, Boosting, and neural network, have been widely used in applications. Their performance is typic…
Adaptive Minimax Estimation over Sparse -Hulls
Zhan Wang, Sandra Paterlini, Frank Gao +1
Given a dictionary of initial estimates of the unknown true regression function, we aim to construct linearly aggregated estimators that target the best performance among all…