Lijing Wang — accepted AI-conference papers

2023

Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts

Aniruddha Adiga, Gursharn Kaur, Lijing Wang, Benjamin Hurt, Przemyslaw Porebski, Srinivasan Venkatramanan +2

AAAI 2023technical

Despite hundreds of methods published in the literature, forecasting epidemic dynamics remains challenging yet important. The challenges stem from multiple sources, including: the need for timely data, co-evolution of epidemic dynamics with behavioral and immunological adaptations, and the evolution…

Cited by 4 Source ↗PDF ↗Scholar ↗

2023

Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models

Lijing Wang, Yingya Li, Timothy Miller, Steven Bethard, Guergana Savova

ACL 2023long

The bias-variance tradeoff is the idea that learning methods need to balance model complexity with data size to minimize both under-fitting and over-fitting. Recent empirical work and theoretical analysis with over-parameterized neural networks challenges the classic bias-variance trade-off notion s…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2020

Wisdom of the Ensemble: Improving Consistency of Deep Learning Models

Lijing Wang, Dipanjan Ghosh, Maria Gonzalez Diaz, Ahmed Farahat, Mahbubul Alam, Chetan Gupta +2

NeurIPS 2020poster

Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant behavior. From an AI model perspective it means given the same input the user would expect the same output, especially for c…

Cited by 16 Source ↗PDF ↗code ↗Scholar ↗