101 citations · 389 across the 29 of their papers we have counts for
4 papers · 1 filter
Generative Multi-Adversarial Networks
Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \emph{Generative Multi-Ad…
Online Monotone Optimization
Ian Gemp, Sridhar Mahadevan
This paper presents a new framework for analyzing and designing no-regret algorithms for dynamic (possibly adversarial) systems. The proposed framework generalizes the popular onli…
Inverting Variational Autoencoders for Improved Generative Accuracy
Ian Gemp, Ishan Durugkar, Mario Parente +2
Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets () to large unlabeled…
Deep Reinforcement Learning With Macro-Actions
Ishan P. Durugkar, Clemens Rosenbaum, Stefan Dernbach +1
Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the At…