832 citations · 1.3k across the 79 of their papers we have counts for
8 papers · 1 filter
Stochastic Feedforward Neural Networks: Universal Approximation
Thomas Merkh, Guido Montúfar
In this chapter we take a look at the universal approximation question for stochastic feedforward neural networks. In contrast to deterministic networks, which represent mappings f…
How Well Do WGANs Estimate the Wasserstein Metric?
Anton Mallasto, Guido Montúfar, Augusto Gerolin
Generative modelling is often cast as minimizing a similarity measure between a data distribution and a model distribution. Recently, a popular choice for the similarity measure ha…
Kernelized Wasserstein Natural Gradient
Michael Arbel, Arthur Gretton, Wuchen Li +1
Many machine learning problems can be expressed as the optimization of some cost functional over a parametric family of probability distributions. It is often beneficial to solve s…
Wasserstein Diffusion Tikhonov Regularization
Alex Tong Lin, Yonatan Dukler, Wuchen Li +1
We propose regularization strategies for learning discriminative models that are robust to in-class variations of the input data. We use the Wasserstein-2 geometry to capture seman…
Optimal Transport to a Variety
T. Ö. Çelik, A. Jamneshan, G. Montúfar +2
We study the problem of minimizing the Wasserstein distance between a probability distribution and an algebraic variety. We consider the setting of finite state spaces and describe…
Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma +3
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs…
Factorized Mutual Information Maximization
Thomas Merkh, Guido Montúfar
We investigate the sets of joint probability distributions that maximize the average multi-information over a collection of margins. These functionals serve as proxies for maximizi…
Decentralized Multi-Agents by Imitation of a Centralized Controller
Alex Tong Lin, Mark J. Debord, Katia Estabridis +3
We consider a multi-agent reinforcement learning problem where each agent seeks to maximize a shared reward while interacting with other agents, and they may or may not be able to…