activity
20172025
most citedAnnealed Generative Adversarial Networks

9 citations · 33 across the 16 of their papers we have counts for

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11 papers · 1 filter

stat.ML20211 cited

Federated Learning as a Mean-Field Game

Arash Mehrjou

We establish a connection between federated learning, a concept from machine learning, and mean-field games, a concept from game theory and control theory. In this analogy, the loc…

stat.ML2020

Learning Dynamical Systems using Local Stability Priors

Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf

A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed.…

stat.ML2019

Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet +1

Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty i…

stat.ML2019

Dual Instrumental Variable Regression

Krikamol Muandet, Arash Mehrjou, Si Kai Lee +1

We present a novel algorithm for non-linear instrumental variable (IV) regression, DualIV, which simplifies traditional two-stage methods via a dual formulation. Inspired by proble…

stat.ML2019

The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou +2

We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple…

stat.ML2018

A Local Information Criterion for Dynamical Systems

Arash Mehrjou, Friedrich Solowjow, Sebastian Trimpe +1

Encoding a sequence of observations is an essential task with many applications. The encoding can become highly efficient when the observations are generated by a dynamical system.…