5 papers
Expert Divergence Learning for MoE-based Language Models
Jiaang Li, Haibin Chen, Langming Liu +9
The Mixture-of-Experts (MoE) architecture is a powerful technique for scaling language models, yet it often suffers from expert homogenization, where experts learn redundant functi…
HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure
Ren-Xin Zhao, Xinze Tong, Shi Wang
Parameterized Quantum Circuits (PQCs) with fixed structures severely degrade the performance of Quantum Machine Learning (QML). To address this, a Hybrid Quantum-Classical Classifi…
Noise-Adaptive Conformal Classification with Marginal Coverage
Teresa Bortolotti, Y. X. Rachel Wang, Xin Tong +3
Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated prediction sets with precise coverage gua…
Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
Ziyi Liang, Tianmin Xie, Xin Tong +1
We develop a conformal inference method to construct a joint confidence region for a given group of missing entries within a sparsely observed matrix, focusing primarily on entries…
Adaptive conformal classification with noisy labels
Matteo Sesia, Y. X. Rachel Wang, Xin Tong
This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, leading to mor…