43 citations · 249 across the 65 of their papers we have counts for
9 papers · 1 filter
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…