56 citations · 142 across the 8 of their papers we have counts for
8 papers
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process
Kevin Du, Ian Gemp, Yi Wu +1
Reinforcement learning has recently been used to approach well-known NP-hard combinatorial problems in graph theory. Among these problems, Hamiltonian cycle problems are exceptiona…
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
Bo Liu, Ian Gemp, Mohammad Ghavamzadeh +3
In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference…
Techniques for Interpretable Machine Learning
Mengnan Du, Ninghao Liu, Xia Hu
Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particu…
Online Monotone Games
Ian Gemp, Sridhar Mahadevan
Algorithmic game theory (AGT) focuses on the design and analysis of algorithms for interacting agents, with interactions rigorously formalized within the framework of games. Result…
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…