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cs.LG2026
Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
Trinh Pham, Viet Huynh, Hongzhi Yin +2
The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released mo…
cs.LG2026
Neural Autoregressive Flows for Markov Boundary Learning
Khoa Nguyen, Bao Duong, Viet Huynh +1
Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial in many applications. While recent advances i…
cs.LG2025
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee
Khoa Nguyen, Viet Huynh, Binh Tran +3
Bayesian Optimization (BO) is a well-established method for addressing black-box optimization problems. In many real-world scenarios, optimization often involves multiple functions…