9 citations · 24 across the 13 of their papers we have counts for
3 papers · 1 filter
ReMix: Calibrated Resampling for Class Imbalance in Deep learning
Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz
Class imbalance is a problem of significant importance in applied deep learning where trained models are exploited for decision support and automated decisions in critical areas su…
Active Measure Reinforcement Learning for Observation Cost Minimization
Colin Bellinger, Rory Coles, Mark Crowley +1
Standard reinforcement learning (RL) algorithms assume that the observation of the next state comes instantaneously and at no cost. In a wide variety of sequential decision making…
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Colin Bellinger, Rory Coles, Mark Crowley +1
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of…