2 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
Jie Hu, Lingyun Chen, Geeho Kim +3
History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on f…
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Jie Hu, Yi-Ting Ma, Do Young Eun
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected grap…
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD
Jie Hu, Yi-Ting Ma, Do Young Eun
Distributed learning is essential to train machine learning algorithms across heterogeneous agents while maintaining data privacy. We conduct an asymptotic analysis of Unified Dist…
Efficiency Ordering of Stochastic Gradient Descent
Jie Hu, Vishwaraj Doshi, Do Young Eun
We consider the stochastic gradient descent (SGD) algorithm driven by a general stochastic sequence, including i.i.d noise and random walk on an arbitrary graph, among others; and…