4 citations · 4 across the 5 of their papers we have counts for
11 papers
From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
Hasan Amin, Harry Yizhou Tian, Xiaoni Duan +3
Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a faithful estimator of human per…
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration
Hasan Amin, Ming Yin, Rajiv Khanna
In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration, yet it often comes at the cost of decrease…
Why Some Models Resist Unlearning: A Linear Stability Perspective
Wei-Kai Chang, Rajiv Khanna
Machine unlearning, the ability to erase the effect of specific training samples without retraining from scratch, is critical for privacy, regulation, and efficiency. However, most…
From Logits to Latents: Contrastive Representation Shaping for LLM Unlearning
Haoran Tang, Rajiv Khanna
Most LLM unlearning methods aim to approximate retrain-from-scratch behaviors with minimal distribution shift, often via alignment-style objectives defined in the prediction space.…
Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes
Wei-Kai Chang, Rajiv Khanna
As deep learning models continue to scale, the growing computational demands have amplified the need for effective coreset selection techniques. Coreset selection aims to accelerat…
A Unified Stability Analysis of SAM vs SGD: Role of Data Coherence and Emergence of Simplicity Bias
Wei-Kai Chang, Rajiv Khanna
Understanding the dynamics of optimization in deep learning is increasingly important as models scale. While stochastic gradient descent (SGD) and its variants reliably find soluti…