261 citations · 600 across the 44 of their papers we have counts for
13 papers · 1 filter
Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs
Yanqi Xu, Hui Dai, Carlos Fernandez-Granda +2
Survival models can model time-to-event outcomes using partially observed data. They are widely used in clinical prediction, including cancer risk, disease progression, treatment r…
Black Box Causal Inference: Effect Estimation via Meta Prediction
Lucius E. J. Bynum, Aahlad Manas Puli, Diego Herrero-Quevedo +4
Causal inference and the estimation of causal effects plays a central role in decision-making across many areas, including healthcare and economics. Estimating causal effects typic…
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
Qidong Yang, Weicheng Zhu, Joseph Keslin +3
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often de…
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda +1
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. The data representations are able to capture many underlyi…
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Boyang Yu, Aakash Kaku, Kangning Liu +7
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI model…
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse
Xiao Li, Sheng Liu, Jinxin Zhou +4
With the ever-increasing complexity of large-scale pre-trained models coupled with a shortage of labeled data for downstream training, transfer learning has become the primary appr…
Avoiding spurious correlations via logit correction
Sheng Liu, Xu Zhang, Nitesh Sekhar +3
Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labe…
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
Avinash Parnandi, Aakash Kaku, Anita Venkatesan +7
Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetit…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
Cramér-Rao bound-informed training of neural networks for quantitative MRI
Xiaoxia Zhang, Quentin Duchemin, Kangning Liu +4
Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-li…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian +1
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed…
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
Aakash Kaku, Sreyas Mohan, Avinash Parnandi +2
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of suc…