29 citations · 35 across the 14 of their papers we have counts for
15 papers
DePICT: Decision-Preserving Interface for Constrained Downstream Tasks
Utkarsh Grover, Ravi Ranjan, Agoritsa Polyzou +5
A constrained optimization problem may involve a parameter in its objective and active constraints, yet the final decision may remain insensitive to small changes in that parameter…
Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs
Ravi Ranjan, Olivera Kotevska, Agoritsa Polyzou
Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machin…
Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio Models
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Transformer-based automatic speech recognition (ASR) models such as Whisper are highly accurate, but their predictions remain difficult to interpret. Existing explainable AI (XAI)…
PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while maintaining diagnostic correc…
VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models
Ravi Ranjan, Agoritsa Polyzou
Vision-language-action (VLA) models are emerging as embodied foundation models for robotic manipulation, but their deployment introduces a new unlearning challenge: removing unsafe…
G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMs
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Large language models (LLMs) are trained on massive web-scale corpora, raising growing concerns about privacy and copyright. Membership inference attacks (MIAs) aim to determine wh…
CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs
Ravi Ranjan, Utkarsh Grover, Mayur Akewar +2
Large Language Models (LLMs) are deployed in high-stakes settings but can show demographic, gender, and geographic biases that undermine fairness and trust. Prior debiasing methods…
Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies
Utkarsh Grover, Ravi Ranjan, Mingyang Mao +9
Deploying foundation models in embodied edge systems is fundamentally a systems problem, not just a problem of model compression. Real-time control must operate within strict size,…
RAZOR: Ratio-Aware Layer Editing for Targeted Unlearning in Vision Transformers and Diffusion Models
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Transformer based diffusion and vision-language models have achieved remarkable success; yet, efficiently removing undesirable or sensitive information without retraining remains a…
Aurora: Neuro-Symbolic AI Driven Advising Agent
Lorena Amanda Quincoso Lugones, Christopher Kverne, Nityam Sharadkumar Bhimani +4
Academic advising in higher education is under severe strain, with advisor-to-student ratios commonly exceeding 300:1. These structural bottlenecks limit timely access to guidance,…
How Good Are Large Language Models for Course Recommendation in MOOCs?
Boxuan Ma, Md Akib Zabed Khan, Tianyuan Yang +2
Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potentia…
FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation
Agoritsa Polyzou, Maria Kalantzi, George Karypis
Course selection is challenging for students in higher educational institutions. Existing course recommendation systems make relevant suggestions to the students and help them in e…
FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning
Maria Kalantzi, Agoritsa Polyzou, George Karypis
Automated Team Formation is becoming increasingly important for a plethora of applications in open source community projects, remote working platforms, as well as online educationa…
Causal Inference in Higher Education: Building Better Curriculums
Prableen Kaur, Agoritsa Polyzou, George Karypis
Higher educational institutions constantly look for ways to meet students' needs and support them through graduation. Recent work in the field of learning analytics have developed…
Grade prediction with course and student specific models
Agoritsa Polyzou, George Karypis
The accurate estimation of students' grades in future courses is important as it can inform the selection of next term's courses and create personalized degree pathways to facilita…