6 papers
Robust Fair Disease Diagnosis in CT Images
Justin Li, Daniel Ding, Asmita Yuki Pritha +3
Automated diagnosis from chest CT has improved considerably with deep learning, but models trained on skewed datasets tend to perform unevenly across patient demographics. However,…
Robust Multi-Source Covid-19 Detection in CT Images
Asmita Yuki Pritha, Jason Xu, Daniel Ding +4
Deep learning models for COVID-19 detection from chest CT scans generally perform well when the training and test data originate from the same institution, but they often struggle…
UniCoMTE: A Universal Counterfactual Framework for Explaining Time-Series Classifiers on ECG Data
Justin Li, Efe Sencan, Jasper Zheng Duan +3
Machine learning models, particularly deep neural networks, have demonstrated strong performance in classifying complex time series data. However, their black-box nature limits tru…
Rethinking Individual Fairness in Deepfake Detection
Aryana Hou, Li Lin, Justin Li +1
Generative AI models have substantially improved the realism of synthetic media, yet their misuse through sophisticated DeepFakes poses significant risks. Despite recent advances i…
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal +54
The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and che…
On Achieving Optimal Adversarial Test Error
Justin D. Li, Matus Telgarsky
We first elucidate various fundamental properties of optimal adversarial predictors: the structure of optimal adversarial convex predictors in terms of optimal adversarial zero-one…