68 citations · 117 across the 15 of their papers we have counts for
10 papers · 1 filter
OT on the Map: Quantifying Domain Shifts in Geographic Space
Haoran Zhang, Livia Betti, Konstantin Klemmer +2
In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data coverage and distribution shifts…
Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations
Olawale Salaudeen, Haoran Zhang, Kumail Alhamoud +2
Benchmarks for out-of-distribution (OOD) generalization frequently show a strong positive correlation between in-distribution (ID) and OOD accuracy across models, termed "accuracy-…
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott, Haoran Zhang, Lasse Hyldig Hansen +2
In machine learning (ML), a widespread claim is that the area under the precision-recall curve (AUPRC) is a superior metric for model comparison to the area under the receiver oper…
Change is Hard: A Closer Look at Subpopulation Shift
Yuzhe Yang, Haoran Zhang, Dina Katabi +1
Machine learning models often perform poorly on subgroups that are underrepresented in the training data. Yet, little is understood on the variation in mechanisms that cause subpop…
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi +1
Machine learning models frequently experience performance drops under distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors such as changes…
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh +3
Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc exp…
Improving the Fairness of Chest X-ray Classifiers
Haoran Zhang, Natalie Dullerud, Karsten Roth +3
Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has…
Learning Optimal Predictive Checklists
Haoran Zhang, Quaid Morris, Berk Ustun +1
Checklists are simple decision aids that are often used to promote safety and reliability in clinical applications. In this paper, we present a method to learn checklists for clini…
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang +1
Machine learning models achieve state-of-the-art performance on many supervised learning tasks. However, prior evidence suggests that these models may learn to rely on shortcut bia…
An Empirical Framework for Domain Generalization in Clinical Settings
Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari +3
Clinical machine learning models experience significantly degraded performance in datasets not seen during training, e.g., new hospitals or populations. Recent developments in doma…