A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning. - Weishi Shi; Dayou Yu; Qi Yu 001 (NeurIPS, 2021)

STARS: Spatial-Temporal Active Re-sampling for Label-Efficient Learning from Noisy Annotations. - Dayou Yu; Weishi Shi; Qi Yu 001 (AAAI, 2023)

Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. - Dayou Yu; Weishi Shi; Qi Yu 001 (ICML, 2023)

Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice. - Dayou Yu; Weishi Shi; Qi Yu 001 (NeurIPS, 2023)

Balancing Feature Similarity and Label Variability for Optimal Size-Aware One-shot Subset Selection. - Abhinab Acharya; Dayou Yu; Qi Yu 001; Xumin Liu (ICML, 2024)

Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity. - Dayou Yu; Minghao Li; Weishi Shi; Qi Yu 001 (NeurIPS, 2024)

Deep energy-pressure regression for a thermodynamically consistent EOS model. - Dayou Yu; Deep Shankar Pandey; Joshua Hinz; Deyan I. Mihaylov; Valentin V. Karasiev; S. X. Hu; Qi Yu 001 (Mach. Learn. Sci. Technol., 2024)