21 citations · 29 across the 7 of their papers we have counts for
9 papers · 1 filter
GrowSpace: Learning How to Shape Plants
Yasmeen Hitti, Ionelia Buzatu, Manuel Del Verme +3
Plants are dynamic systems that are integral to our existence and survival. Plants face environment changes and adapt over time to their surrounding conditions. We argue that plant…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…
Deep interpretability for GWAS
Deepak Sharma, Audrey Durand, Marc-André Legault +4
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In these studies, association testing is done…
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
Nicolas Garneau, Mathieu Godbout, David Beauchemin +2
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We…
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani, Abbas Mehrabian, Audrey Durand +1
We propose , a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampl…
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Thang Doan, Bogdan Mazoure, Moloud Abdar +3
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, w…