Audrey Durand | DeepAI

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Doina Precup
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∙ 04/26/2023

Association Rules Mining with Auto-Encoders

Association rule mining is one of the most studied research fields of da...

0 Théophile Berteloot, et al. ∙

∙ 02/19/2023

Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

Cancer treatment is an arduous process for patients and causes many side...

0 Rupali Bhati, et al. ∙

∙ 12/10/2022

Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy

Polypharmacy, most often defined as the simultaneous consumption of five...

0 Alexandre Larouche, et al. ∙

∙ 11/23/2022

Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining

Association rule mining is one of the most studied research fields of da...

0 Théophile Berteloot, et al. ∙

∙ 10/15/2021

GrowSpace: Learning How to Shape Plants

Plants are dynamic systems that are integral to our existence and surviv...

0 Yasmeen Hitti, et al. ∙

∙ 03/22/2021

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

Multi-armed bandit algorithms have been argued for decades as useful for...

0 Joseph Jay Williams, et al. ∙

∙ 11/03/2020

Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model

The objective of this work was to assess the clinical performance of an ...

0 Sophie-Camille Hogue, et al. ∙

∙ 07/03/2020

Deep interpretability for GWAS

Genome-Wide Association Studies are typically conducted using linear mod...

1 Deepak Sharma, et al. ∙

∙ 12/03/2019

A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well

In this paper, we reproduce the experiments of Artetxe et al. (2018b) re...

0 Nicolas Garneau, et al. ∙

∙ 10/11/2019

Old Dog Learns New Tricks: Randomized UCB for Bandit Problems

We propose RandUCB, a bandit strategy that uses theoretically derived co...

0 Sharan Vaswani, et al. ∙

∙ 09/17/2019

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Continuous control tasks in reinforcement learning are important because...

15 Thang Doan, et al. ∙

∙ 05/16/2019

Leveraging exploration in off-policy algorithms via normalizing flows

Exploration is a crucial component for discovering approximately optimal...

0 Bogdan Mazoure, et al. ∙

∙ 11/01/2018

Temporal Regularization in Markov Decision Process

Several applications of Reinforcement Learning suffer from instability d...

0 Pierre Thodoroff, et al. ∙

∙ 07/31/2018

Online Adaptative Curriculum Learning for GANs

Generative Adversarial Networks (GANs) can successfully learn a probabil...

20 Thang Doan, et al. ∙

∙ 03/28/2018

Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy

With super-resolution optical microscopy, it is now possible to observe ...

0 Louis-Émile Robitaille, et al. ∙

∙ 09/13/2017

Query Completion Using Bandits for Engines Aggregation

Assisting users by suggesting completed queries as they type is a common...

0 Audrey Durand, et al. ∙

∙ 08/02/2017

Streaming kernel regression with provably adaptive mean, variance, and regularization

We consider the problem of streaming kernel regression, when the observa...

0 Audrey Durand, et al. ∙

∙ 01/04/2017

Estimating Quality in Multi-Objective Bandits Optimization

Many real-world applications are characterized by a number of conflictin...

0 Audrey Durand, et al. ∙

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