6 papers
Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations
Stephen E. Farr, Gianni De Fabritiis
Alchemical relative binding free energy (RBFE) calculations are limited by the fixed-charge approximation of classical force fields. Hybrid machine learning interatomic potential/m…
Fast Mixing for Low-Temperature Potts Models via Poisson Trees
Zongchen Chen, Andreas Galanis, Leslie Ann Goldberg +3
The -state ferromagnetic Potts model on a graph is a probability distribution on all -colourings of that favours many monochromatic edges. Approximate sampling from t…
Uniqueness and Mixing in the Low-Temperature Random-Cluster Model on Trees and Random Graphs
Antonio Blanca, Reza Gheissari, Heehyun Park +1
We study the random-cluster model on trees and treelike graphs at low temperatures. This is a model of dependent percolation parametrized by an edge probability and a…
Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning
Xusheng Zhang, Tuan Nguyen, Ting He
We consider the design of mixing matrices to minimize the operation cost for decentralized federated learning (DFL) in wireless networks, with focus on minimizing the maximum per-n…
One-Shot Learning for k-SAT
Andreas Galanis, Leslie Ann Goldberg, Xusheng Zhang
Consider a -SAT formula where every variable appears at most times. Let be a satisfying assignment, sampled proportionally to where is the nu…
Mean-field Potts and random-cluster dynamics from high-entropy initializations
Antonio Blanca, Reza Gheissari, Xusheng Zhang
A common obstruction to efficient sampling from high-dimensional distributions with Markov chains is the multimodality of the target distribution because they may get trapped far f…