15 citations · 31 across the 7 of their papers we have counts for
7 papers
Reinforcement Learning with Exogenous States and Rewards
George Trimponias, Thomas G. Dietterich
Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal. This paper formalizes exogenous state variables an…
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
Zewei Chen, Fengwei Zhou, George Trimponias +1
Multi-objective Neural Architecture Search (NAS) aims to discover novel architectures in the presence of multiple conflicting objectives. Despite recent progress, the problem of ap…
Comparing EM with GD in Mixture Models of Two Components
Guojun Zhang, Pascal Poupart, George Trimponias
The expectation-maximization (EM) algorithm has been widely used in minimizing the negative log likelihood (also known as cross entropy) of mixture models. However, little is under…
Node-Constrained Traffic Engineering: Theory and Applications
George Trimponias, Yan Xiao, Xiaorui Wu +2
Traffic engineering (TE) is a fundamental task in networking. Conventionally, traffic can take any path connecting the source and destination. Emerging technologies such as segment…
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Thomas G. Dietterich, George Trimponias, Zhitang Chen
Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and re…
Centrality-based Middlepoint Selection for Traffic Engineering with Segment Routing
George Trimponias, Yan Xiao, Hong Xu +2
Segment routing is an emerging technology to simplify traffic engineering implementation in WANs. It expresses an end-to-end logical path as a sequence of segments through a set of…
Dynamic Sum Product Networks for Tractable Inference on Sequence Data (Extended Version)
Mazen Melibari, Pascal Poupart, Prashant Doshi +1
Sum-Product Networks (SPN) have recently emerged as a new class of tractable probabilistic graphical models. Unlike Bayesian networks and Markov networks where inference may be exp…