108 citations · 226 across the 60 of their papers we have counts for
19 papers · 1 filter
MAdam: Metric-Aware Multi-Objective Adam
Fengbei Liu, Rachit Saluja, Sunwoo Kwak +5
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost univers…
AI-assisted workflow enables rapid, high-fidelity breast cancer clinical trial eligibility prescreening
Jacob T. Rosenthal, Emma Hahesy, Sulov Chalise +4
Clinical trials play an important role in cancer care and research, yet participation rates remain low. We developed MSK-MATCH (Memorial Sloan Kettering Multi-Agent Trial Coordinat…
Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing
Minh Nguyen, Mert R. Sabuncu
Invariant causal prediction (ICP) is a popular technique for finding causal parents (direct causes) of a target via exploiting distribution shifts and invariance testing (Peters et…
Adapting to Shifting Correlations with Unlabeled Data Calibration
Minh Nguyen, Alan Q. Wang, Heejong Kim +1
Distribution shifts between sites can seriously degrade model performance since models are prone to exploiting unstable correlations. Thus, many methods try to find features that a…
Assessing the significance of longitudinal data in Alzheimer's Disease forecasting
Batuhan K. Karaman, Mert R. Sabuncu
In this study, we employ a transformer encoder model to characterize the significance of longitudinal patient data for forecasting the progression of Alzheimer's Disease (AD). Our…
Knockout: A simple way to handle missing inputs
Minh Nguyen, Batuhan K. Karaman, Heejong Kim +3
Deep learning models benefit from rich (e.g., multi-modal) input features. However, multimodal models might be challenging to deploy, because some inputs may be missing at inferenc…