43 citations · 249 across the 65 of their papers we have counts for
67 papers
StraightDP: Geometry-Aware Differential Privacy for Rectified-Flow Transformers
Xujun Che, Depeng Xu, Xintao Wu
Differentially private (DP) training of text-conditioned generative models suffers a utility cliff at strong privacy. We revisit this problem through the geometry of rectified flow…
BDIP-Net: Dual-Interaction Graph Learning for Property Prediction of Bilayer Materials
An Vuong, Chen Zhao, Jin Hu +2
Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions. The…
MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
Dong Li, Yanchi Liu, Xujiang Zhao +6
Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their li…
Coupled Hierarchical Search over Topology and Execution for Agentic Workflow Synthesis
Dong Li, Yanchi Liu, Xujiang Zhao +6
Although structured workflows empower Large Language Models (LLMs) to tackle complex problems, automating their creation is severely hindered by a vast combinatorial search space,…
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
An Vuong, Minh-Hao Van, Chen Zhao +1
AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions. Bilayer 2D material stacking…
Leveraging Foundation Models for Causal Generative Modeling
Aneesh Komanduri, Xintao Wu
Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approaches focus on integrating causa…
How Does Differential Privacy Affect Social Bias in LLMs? A Systematic Evaluation
Eduardo Tenorio, Karuna Bhaila, Xintao Wu
Large language models (LLMs) trained on web-scale corpora can memorize sensitive training data, posing significant privacy risks. Differential privacy (DP) has emerged as a princip…
Soft Prompting for Unlearning in Large Language Models
Karuna Bhaila, Minh-Hao Van, Xintao Wu
The widespread popularity of Large Language Models (LLMs), partly due to their unique ability to perform in-context learning, has also brought to light the importance of ethical an…
Privacy Preserving Prompt Engineering: A Survey
Kennedy Edemacu, Xintao Wu
Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a…
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
Aneesh Komanduri, Chen Zhao, Feng Chen +1
Diffusion probabilistic models (DPMs) have become the state-of-the-art in high-quality image generation. However, DPMs have an arbitrary noisy latent space with no interpretable or…
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu +1
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have compar…
Robust Influence-based Training Methods for Noisy Brain MRI
Minh-Hao Van, Alycia N. Carey, Xintao Wu
Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on classical image processing or d…
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
Minh-Hao Van, Prateek Verma, 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 emergent abilitie…
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
Chen Zhao, Feng Mi, Xintao Wu +3
The fairness-aware online learning framework has emerged as a potent tool within the context of continuous lifelong learning. In this scenario, the learner's objective is to progre…
In-Context Learning Demonstration Selection via Influence Analysis
Vinay M. S., Minh-Hao Van, Xintao Wu
Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages,…
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao +3
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced…
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…