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23 papers · 2 filters
Projected Stochastic Gradient Langevin Algorithms for Constrained Sampling and Non-Convex Learning
Andrew Lamperski
Langevin algorithms are gradient descent methods with additive noise. They have been used for decades in Markov chain Monte Carlo (MCMC) sampling, optimization, and learning. Their…
Sheaf Neural Networks
Jakob Hansen, Thomas Gebhart
We present a generalization of graph convolutional networks by generalizing the diffusion operation underlying this class of graph neural networks. These sheaf neural networks are…
Hybrid Federated Learning: Algorithms and Implementation
Xinwei Zhang, Wotao Yin, Mingyi Hong +1
Federated learning (FL) is a recently proposed distributed machine learning paradigm dealing with distributed and private data sets. Based on the data partition pattern, FL is ofte…
-GAIL: Learning -Divergence for Generative Adversarial Imitation Learning
Xin Zhang, Yanhua Li, Ziming Zhang +1
Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algori…
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Jared D. Willard, Jordan S. Read, Alison P. Appling +3
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitor…
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity
Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos
Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset o…