Statistical Learning
First Order Stochastic Optimization with Oblivious Noise
We initiate the study of stochastic optimization with oblivious noise, broadly generalizing the standard heavy-tailed noise setup.
Probabilistic Graph Rewiring via Virtual Nodes
MPNNs have been studied extensively in theory and practice [Bรถker et al., 2023, Gilmer et al., 2017,
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
We propose a new exact minibatch MH method, TunaMH, which exposes a tunable trade-off between its batch size and its theoretically guaranteed convergence rate.
Graph Convolutional Kernel Machine versus Graph Convolutional Networks
An example is the graph convolutional kernel support vector machine (GCKSVM) for node classification, for which we analyze the generalization error bound and discuss the impact of the graph structure.
QuantumAlgorithmsforSamplingLog-Concave DistributionsandEstimatingNormalizingConstants
Given a convex function f: Rd R, the problem of sampling from a distribution e f(x) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc.
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
In this work, we empirically investigate the impact of neural parameter symmetries by introducing new neural network architectures that have reduced parameter space symmetries.