9 citations · 19 across the 16 of their papers we have counts for
18 papers
Evian: Towards Explainable Visual Instruction-tuning Data Auditing
Zimu Jia, Mingjie Xu, Andrew Estornell +1
The efficacy of Large Vision-Language Models (LVLMs) is critically dependent on the quality of their training data, requiring a precise balance between visual fidelity and instruct…
Any-Depth Alignment: Unlocking Innate Safety Alignment of LLMs to Any-Depth
Jiawei Zhang, Andrew Estornell, David D. Baek +2
Large Language Models (LLMs) exhibit strong but shallow alignment: they directly refuse harmful queries when a refusal is expected at the very start of an assistant turn, yet this…
AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation
Xianyang Liu, Yilin Liu, Shuai Wang +5
The creation of high-quality datasets to improve Large Language Model (LLM) reasoning remains a significant challenge, as current methods often suffer from generating low-quality/i…
How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs
Andrew Estornell, Jean-Francois Ton, Muhammad Faaiz Taufiq +1
Large Language Models (LLMs) have achieved strong performance on a wide range of complex reasoning tasks, yet further gains are often possible by leveraging the complementary stren…
Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring
Mingjie Xu, Andrew Estornell, Hongzheng Yang +4
The application of visual instruction tuning and other post-training techniques has significantly enhanced the capabilities of Large Language Models (LLMs) in visual understanding,…
To Give or Not to Give? The Impacts of Strategically Withheld Recourse
Yatong Chen, Andrew Estornell, Yevgeniy Vorobeychik +1
Individuals often aim to reverse undesired outcomes in interactions with automated systems, like loan denials, by either implementing system-recommended actions (recourse), or mani…
ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration
Andrew Estornell, Jean-Francois Ton, Yuanshun Yao +1
Large language models (LLMs) have demonstrated a remarkable ability to serve as general-purpose tools for various language-based tasks. Recent works have demonstrated that the effi…
Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets
Victor Borza, Andrew Estornell, Ellen Wright Clayton +4
Large participatory biomedical studies, studies that recruit individuals to join a dataset, are gaining popularity and investment, especially for analysis by modern AI methods. Bec…
User-Creator Feature Polarization in Recommender Systems with Dual Influence
Tao Lin, Kun Jin, Andrew Estornell +3
Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems natura…
Dataset Representativeness and Downstream Task Fairness
Victor Borza, Andrew Estornell, Chien-Ju Ho +2
Our society collects data on people for a wide range of applications, from building a census for policy evaluation to running meaningful clinical trials. To collect data, we typica…
Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh +2
In today's competitive financial landscape, understanding and anticipating customer goals is crucial for institutions to deliver a personalized and optimized user experience. This…
Measuring and Reducing LLM Hallucination without Gold-Standard Answers
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton +3
LLM hallucination, i.e. generating factually incorrect yet seemingly convincing answers, is currently a major threat to the trustworthiness and reliability of LLMs. The first step…
Manipulating Elections by Changing Voter Perceptions
Junlin Wu, Andrew Estornell, Lecheng Kong +1
The integrity of elections is central to democratic systems. However, a myriad of malicious actors aspire to influence election outcomes for financial or political benefit. A commo…
Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents
Andrew Estornell, Sanmay Das, Yang Liu +1
The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these decision making syst…
Incentivizing Truthfulness Through Audits in Strategic Classification
Andrew Estornell, Sanmay Das, Yevgeniy Vorobeychik
In many societal resource allocation domains, machine learning methods are increasingly used to either score or rank agents in order to decide which ones should receive either reso…
Election Control by Manipulating Issue Significance
Andrew Estornell, Sanmay Das, Edith Elkind +1
Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors. The study of algorithmic complexity of the problem of manipulating elect…
Deception through Half-Truths
Andrew Estornell, Sanmay Das, Yevgeniy Vorobeychik
Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politica…
PBW deformations of quadratic monomial algebras
Zachary Cline, Andrew Estornell, Chelsea Walton +1
A result of Braverman and Gaitsgory from 1996 gives necessary and sufficient conditions for a filtered algebra to be a Poincaré-Birkhoff-Witt (PBW) deformation of a Koszul algebra.…