
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
The distributional reinforcement learning (RL) approach advocates for re...
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A Probabilistic ForecastDriven Strategy for a RiskAware Participation in the Capacity Firming Market
This paper addresses the energy management of a gridconnected renewable...
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Neural Empirical Bayes: Source Distribution Estimation and its Applications to SimulationBased Inference
We revisit empirical Bayes in the absence of a tractable likelihood func...
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LightningFast Gravitational Wave Parameter Inference through Neural Amortization
Gravitational waves from compact binaries measured by the LIGO and Virgo...
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Graphical Normalizing Flows
Normalizing flows model complex probability distributions by combining a...
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You say Normalizing Flows I see Bayesian Networks
Normalizing flows have emerged as an important family of deep neural net...
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Unconstrained Monotonic Neural Networks
Monotonic neural networks have recently been proposed as a way to define...
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Recurrent machines for likelihoodfree inference
Likelihoodfree inference is concerned with the estimation of the parame...
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Antoine Wehenkel
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