360 citations · 2.7k across the 150 of their papers we have counts for
9 papers · 2 filters
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia +3
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representatio…
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, Doina Precup
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for…
Temporal Regularization in Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau +1
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly…
Combined Reinforcement Learning via Abstract Representations
Vincent François-Lavet, Yoshua Bengio, Doina Precup +1
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly…
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
Lara J. Kanbar, Charles C. Onu, Wissam Shalish +4
Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mecha…
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing
Charles C. Onu, Lara J. Kanbar, Wissam Shalish +4
Extremely preterm infants commonly require intubation and invasive mechanical ventilation after birth. While the duration of mechanical ventilation should be minimized in order to…
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
Guillaume Rabusseau, Tianyu Li, Doina Precup
In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the case of sequences of discrete…
Dyna Planning using a Feature Based Generative Model
Ryan Faulkner, Doina Precup
Dyna-style reinforcement learning is a powerful approach for problems where not much real data is available. The main idea is to supplement real trajectories, or sequences of sampl…
Learning Safe Policies with Expert Guidance
Jessie Huang, Fa Wu, Doina Precup +1
We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existe…