13 citations · 23 across the 11 of their papers we have counts for
11 papers
The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
Aaryaman Kalani, Murari Mandal, Dhruv Kumar +2
Approximate machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. We identify a previously undocumented co…
OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs
Debdeep Sanyal, Umakanta Maharana, Yash Sinha +4
Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential o…
Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models
Yash Sinha, Manit Baser, Murari Mandal +2
Knowledge erasure in large language models (LLMs) is important for ensuring compliance with data and AI regulations, safeguarding user privacy, mitigating bias, and misinformation.…
Nine Ways to Break Copyright Law and Why Our LLM Won't: A Fair Use Aligned Generation Framework
Aakash Sen Sharma, Debdeep Sanyal, Priyansh Srivastava +4
Large language models (LLMs) commonly risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications, posing significant et…
UnStar: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs
Yash Sinha, Murari Mandal, Mohan Kankanhalli
The key components of machine learning are data samples for training, model for learning patterns, and loss function for optimizing accuracy. Analogously, unlearning can potentiall…
Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints
Yash Sinha, Murari Mandal, Mohan Kankanhalli
User data spread across multiple modalities has popularized multi-modal recommender systems (MMRS). They recommend diverse content such as products, social media posts, TikTok reel…
DPTraj-PM: Differentially Private Trajectory Synthesis Using Prefix Tree and Markov Process
Nana Wang, Mohan Kankanhalli
The increasing use of GPS-enabled devices has generated a large amount of trajectory data. These data offer us vital insights to understand the movements of individuals and populat…
S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing
Guangzhi Wang, Tianyi Chen, Kamran Ghasedi +6
Face attribute editing plays a pivotal role in various applications. However, existing methods encounter challenges in achieving high-quality results while preserving identity, edi…
EcoVal: An Efficient Data Valuation Framework for Machine Learning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +3
Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley va…
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
Xilie Xu, Keyi Kong, Ning Liu +4
The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This pa…
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework
Xilie Xu, Jingfeng Zhang, Mohan Kankanhalli
Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial robustness in downstream applications, without requiring a lot of computational resources and collecting signi…