244 citations · 508 across the 40 of their papers we have counts for
12 papers · 1 filter
SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning
Trisha Das, Zifeng Wang, Afrah Shafquat +4
Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging…
UniPredict: Large Language Models are Universal Tabular Classifiers
Ruiyu Wang, Zifeng Wang, Jimeng Sun
Tabular data prediction is a fundamental machine learning task for many applications. Existing methods predominantly employ discriminative modeling and operate under the assumption…
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Zifeng Wang, Chufan Gao, Cao Xiao +1
Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design whil…
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning
Zifeng Wang, Cao Xiao, Jimeng Sun
Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. Accurate trial outcome prediction based on historical trial data promises better…
TransTab: Learning Transferable Tabular Transformers Across Tables
Zifeng Wang, Jimeng Sun
Tabular data (or tables) are the most widely used data format in machine learning (ML). However, ML models often assume the table structure keeps fixed in training and testing. Bef…
SurvTRACE: Transformers for Survival Analysis with Competing Events
Zifeng Wang, Jimeng Sun
In medicine, survival analysis studies the time duration to events of interest such as mortality. One major challenge is how to deal with multiple competing events (e.g., multiple…
PAC-Bayes Information Bottleneck
Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu +3
Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works tryi…
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Zifeng Wang, Xi Chen, Rui Wen +3
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommen…
Finding Influential Instances for Distantly Supervised Relation Extraction
Zifeng Wang, Rui Wen, Xi Chen +3
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise. Current works based on attent…
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
Zifeng Wang, Rui Wen, Xi Chen +4
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main chall…
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond
Zifeng Wang, Xi Chen, Rui Wen +1
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), wh…
Less Is Better: Unweighted Data Subsampling via Influence Function
Zifeng Wang, Hong Zhu, Zhenhua Dong +2
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.…