3 papers
stat.CO2026
Delocalization of bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin
Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed +1
Unadjusted samplers such as unadjusted Hamiltonian Monte Carlo and underdamped Langevin are well-known to be biased. Metropolis--Hastings adjustment has been conventionally incorpo…
stat.ML2024
Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias
Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed +1
The unadjusted Langevin algorithm is commonly used to sample probability distributions in extremely high-dimensional settings. However, existing analyses of the algorithm for stron…
cs.LG2024
The surprising efficiency of temporal difference learning for rare event prediction
Xiaoou Cheng, Jonathan Weare
We quantify the efficiency of temporal difference (TD) learning over the direct, or Monte Carlo (MC), estimator for policy evaluation in reinforcement learning, with an emphasis on…