44 citations · 55 across the 6 of their papers we have counts for
6 papers
BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap
Shengyuan Hu, Neil Kale, Pratiksha Thaker +3
Machine unlearning has the potential to improve the safety of large language models (LLMs) by removing sensitive or harmful information post hoc. A key challenge in unlearning invo…
AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection
Kai Hua, Steven Wu, Ge Zhang +1
Recently, there has been growing interest in collecting reasoning-intensive pretraining data to improve LLMs' complex reasoning ability. Prior approaches typically rely on supervis…
Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning
Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu +1
Machine unlearning is a promising approach to mitigate undesirable memorization of training data in ML models. However, in this work we show that existing approaches for unlearning…
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna, Tessa Han, Alex Gu +3
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of…
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relev…
Competing Bandits: The Perils of Exploration Under Competition
Guy Aridor, Yishay Mansour, Aleksandrs Slivkins +1
Most online platforms strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information.…