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Found 6 papers, 2 papers with code
Sinkhorn Flow: A Continuous-Time Framework for Understanding and Generalizing the Sinkhorn Algorithm
Many problems in machine learning can be formulated as solving entropy-regularized optimal transport on the space of probability measures.
Isotropic Gaussian Processes on Finite Spaces of Graphs
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
Riemannian stochastic approximation algorithms
We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds.
Online Active Model Selection for Pre-trained Classifiers
Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries?
Stochastic Submodular Maximization: The Case of Coverage Functions
By exploiting that common extensions act linearly on the class of submodular functions, we employ projected stochastic gradient ascent and its variants in the continuous domain, and perform rounding to obtain discrete solutions.
Smart broadcasting: Do you want to be seen?
Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them.
