Audrey Durand

Orcid: 0000-0001-9290-8603

According to our database1, Audrey Durand authored at least 42 papers between 2010 and 2024.

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Bibliography

2024
Deep reinforcement learning for continuous wood drying production line control.
Comput. Ind., January, 2024

Randomized Confidence Bounds for Stochastic Partial Monitoring.
CoRR, 2024

2023
Association Rules Mining with Auto-Encoders.
CoRR, 2023

Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients.
CoRR, 2023

Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract).
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations.
Nat. Mach. Intell., 2022

Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy.
CoRR, 2022

Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining.
CoRR, 2022

Contextual bandit optimization of super-resolution microscopy.
Proceedings of the 35th Canadian Conference on Artificial Intelligence, Toronto, Ontario, 2022

A Game-Theoretic Perspective on Risk-Sensitive Reinforcement Learning.
Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022) co-located with the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), 2022

Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract).
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Exploring polypharmacy with artificial intelligence: data analysis protocol.
BMC Medical Informatics Decis. Mak., 2021

Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders.
J. Am. Medical Informatics Assoc., 2021

GrowSpace: Learning How to Shape Plants.
CoRR, 2021

CARL: Conditional-value-at-risk Adversarial Reinforcement Learning.
CoRR, 2021

Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments.
CoRR, 2021

Routine Bandits: Minimizing Regret on Recurring Problems.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

2020
Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model.
CoRR, 2020

Deep interpretability for GWAS.
CoRR, 2020

A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well.
Proceedings of The 12th Language Resources and Evaluation Conference, 2020

Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Old Dog Learns New Tricks: Randomized UCB for Bandit Problems.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract).
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning.
CoRR, 2019

Leveraging exploration in off-policy algorithms via normalizing flows.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

Leveraging Observations in Bandits: Between Risks and Benefits.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

On-Line Adaptative Curriculum Learning for GANs.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Streaming kernel regression with provably adaptive mean, variance, and regularization.
J. Mach. Learn. Res., 2018

Online Adaptative Curriculum Learning for GANs.
CoRR, 2018

Temporal Regularization for Markov Decision Process.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis.
Proceedings of the Machine Learning for Healthcare Conference, 2018

Leveraging Observational Learning for Exploration in Bandits.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Rating Super-Resolution Microscopy Images With Deep Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Query Completion Using Bandits for Engines Aggregation.
CoRR, 2017

Estimating Quality in User-Guided Multi-Objective Bandits Optimization.
CoRR, 2017

Bayesian optimization for conditional hyperparameter spaces.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2015
Adaptive Treatment Allocation Using Sub-Sampled Gaussian Processes.
Proceedings of the 2015 AAAI Fall Symposia, Arlington, Virginia, USA, November 12-14, 2015, 2015

2014
Bayesian classification and unsupervised learning for isolating weeds in row crops.
Pattern Anal. Appl., 2014

2012
Population-Based Simulation for Public Health: Generic Software Infrastructure and Its Application to Osteoporosis.
IEEE Trans. Syst. Man Cybern. Part A, 2012

Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space.
Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2010
SCHNAPS: a generic population-based simulator for public health purposes.
Proceedings of the SummerSim '10, 2010


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