A. Durand

Publications33
h-index10
Citations283
Highly Influential Citations21
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Ventral Tegmental Area Cannabinoid Type-1 Receptors Control Voluntary Exercise Performance
BACKGROUND We have shown that the endogenous stimulation of cannabinoid type-1 (CB₁) receptors is a prerequisite for voluntary running in mice, but the precise mechanisms through which the… Expand
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  • 5
Streaming kernel regression with provably adaptive mean, variance, and regularization
We consider the problem of streaming kernel regression, when the observations arrive sequentially and the goal is to recover the underlying mean function, assumed to belong to an RKHS. The variance… Expand
  • 16
  • 4
  • Open Access
Neuronal representation of individual heroin choices in the orbitofrontal cortex
Drug addiction is a harmful preference for drug use over and at the expense of other non‐drug‐related activities. We previously identified in the rat orbitofrontal cortex (OFC) a mechanism that… Expand
  • 15
  • 3
Bayesian optimization for conditional hyperparameter spaces
Hyperparameter optimization is now widely applied to tune the hyperparameters of learning algorithms. The hyperparameters can have structure, resulting in hyperparameters depending on conditions, or… Expand
  • 18
  • 2
  • Open Access
Online Adaptative Curriculum Learning for GANs
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and… Expand
  • 13
  • 2
  • Open Access
Thompson Sampling for Combinatorial Bandits and its Application to Online Feature Selection
In this work, we address the combinatorial optimization problem in the stochastic bandit setting with bandit feedback. We propose to use the seminal Thompson Sampling algorithm under an assumption on… Expand
  • 13
  • 2
  • Open Access
Leveraging exploration in off-policy algorithms via normalizing flows
Exploration is a crucial component for discovering approximately optimal policies in most high-dimensional reinforcement learning (RL) settings with sparse rewards. Approaches such as neural density… Expand
  • 10
  • 2
  • Open Access
Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes
Background Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant… Expand
  • 5
  • 1
Bayesian classification and unsupervised learning for isolating weeds in row crops
This paper presents a weed/crop classification method using computer vision and morphological analysis. Subsequent supervised and unsupervised learning methods are applied to extract dominant… Expand
  • 31
  • Open Access
Cocaine Addiction as a Homeostatic Reinforcement Learning Disorder
Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important… Expand
  • 16
  • Open Access