3 papers
cs.CV2026
Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification
Afshar Shamsi, Xiao-Yu Guo, Hamid Alinejad-Rokny +3
Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in the absence of supervision, e…
cs.LG2026
FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
Zhihan Yang, Jiaqi Wei, Xiang Zhang +6
Tabular data high-stakes critical decision-making in domains such as finance, healthcare, and scientific discovery. Yet, learning effectively from tabular data in few-shot settings…
cs.LG2025
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo +6
Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and…