92 citations · 433 across the 58 of their papers we have counts for
64 papers
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
Vineeth Dorna, Anmol Mekala, Wenlong Zhao +4
Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the…
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?
Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton +1
Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs…
Online Data Collection for Efficient Semiparametric Inference
Shantanu Gupta, Zachary C. Lipton, David Childers
While many works have studied statistical data fusion, they typically assume that the various datasets are given in advance. However, in practice, estimation requires difficult dat…
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
Jake Fawkes, Nic Fishman, Mel Andrews +1
Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair". Real-world data, however, are typica…
Towards characterizing the value of edge embeddings in Graph Neural Networks
Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton +3
Graph neural networks (GNNs) are the dominant approach to solving machine learning problems defined over graphs. Despite much theoretical and empirical work in recent years, our un…
Failure Modes of LLMs for Causal Reasoning on Narratives
Khurram Yamin, Shantanu Gupta, Gaurav R. Ghosal +2
The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure…