5 papers
A Distributional Optimisation Perspective on Combining Models in Deep Learning
Congye Wang, Yan Lin, Zheyang Shen +2
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typic…
When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination
Congye Wang
Trimming suspicious calibration points is a common response to contamination in conformal prediction. Its effect on clean-target coverage, however, is governed by the retained law…
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa +2
Sampling algorithms drive probabilistic machine learning, and recent years have seen an explosion in the diversity of tools for this task. However, the increasing sophistication of…
Reinforcement Learning for Adaptive MCMC
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
An informal observation, made by several authors, is that the adaptive design of a Markov transition kernel has the flavour of a reinforcement learning task. Yet, to-date it has re…
Stein -Importance Sampling
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are…