43 citations · 249 across the 65 of their papers we have counts for
12 papers · 1 filter
Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach
Wen Huang, Xintao Wu
This paper studies bandit problems where an agent has access to offline data that might be utilized to potentially improve the estimation of each arm's reward distribution. A major…
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model
Minh-Hao Van, Xintao Wu
Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore more emergent abi…
Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
Chen Zhao, Kai Jiang, Xintao Wu +4
The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricat…
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Aneesh Komanduri, Xintao Wu, Yongkai Wu +1
Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by…
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
Alycia N. Carey, Minh-Hao Van, Xintao Wu
How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate th…
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
Karuna Bhaila, Wen Huang, Yongkai Wu +1
Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protect…
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
Minh-Hao Van, Alycia N. Carey, Xintao Wu
While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, whi…
On Prediction Feature Assignment in the Heckman Selection Model
Huy Mai, Xintao Wu
Under missing-not-at-random (MNAR) sample selection bias, the performance of a prediction model is often degraded. This paper focuses on one classic instance of MNAR sample selecti…
Robust Fraud Detection via Supervised Contrastive Learning
Vinay M. S., Shuhan Yuan, Xintao Wu
Deep learning models have recently become popular for detecting malicious user activity sessions in computing platforms. In many real-world scenarios, only a few labeled malicious…
Towards Fair Disentangled Online Learning for Changing Environments
Chen Zhao, Feng Mi, Xintao Wu +4
In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Alt…
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Aneesh Komanduri, Yongkai Wu, Feng Chen +1
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information f…
A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias
Huy Mai, Wen Huang, Wei Du +1
The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the tr…