Essec\Faculty\Model\Profile {#2216
#_id: "B00812202"
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"slug" => "daudel-kamelia"
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0 => Essec\Faculty\Model\CareerItem {#2219
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]
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"fr" => "France"
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}
1 => Essec\Faculty\Model\CareerItem {#2222
#_index: null
#_id: null
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]
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}
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0 => Essec\Faculty\Model\Diplome {#2218
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#_source: array:6 [
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"fr" => "Télécom Paris"
"en" => "Télécom Paris"
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]
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}
1 => Essec\Faculty\Model\Diplome {#2220
#_index: null
#_id: null
#_source: array:6 [
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]
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}
2 => Essec\Faculty\Model\Diplome {#2217
#_index: null
#_id: null
#_source: array:6 [
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]
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"bio" => array:2 [
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0 => Essec\Faculty\Model\Distinction {#2223
#_index: null
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0 => Essec\Faculty\Model\TeachingItem {#2221
#_index: null
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}
1 => Essec\Faculty\Model\TeachingItem {#2215
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#_source: array:7 [
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"en" => "France"
]
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}
]
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"indexedAt" => "2024-11-21T12:21:22.000Z"
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0 => Essec\Faculty\Model\Contribution {#2225
#_index: "academ_contributions"
#_id: "14316"
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"id" => "14316"
"slug" => "infinite-dimensional-gradient-based-descent-for-alpha-divergence-minimisation"
"yearMonth" => "2021-08"
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"title" => "Infinite-dimensional gradient-based descent for alpha-divergence minimisation"
"description" => "DAUDEL, K., DOUC, R. et PORTIER, F. (2021). Infinite-dimensional gradient-based descent for alpha-divergence minimisation. <i>Annals of Statistics</i>, 49(4), pp. 2250 - 2270."
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"fr" => "We demonstrate empirically on both toy and real-world examples the benefit of using the Power Descent and going beyond the Entropic Mirror Descent framework, which fails as the dimension grows."
"en" => "We demonstrate empirically on both toy and real-world examples the benefit of using the Power Descent and going beyond the Entropic Mirror Descent framework, which fails as the dimension grows."
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1 => Essec\Faculty\Model\Contribution {#2227
#_index: "academ_contributions"
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"slug" => "monotonic-alpha-divergence-minimisation-for-variational-inference"
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"year" => "2023"
"title" => "Monotonic Alpha-divergence Minimisation for Variational Inference"
"description" => "DAUDEL, K., DOUC, R. et ROUEFF, F. (2023). Monotonic Alpha-divergence Minimisation for Variational Inference. <i>Journal of Machine Learning Research</i>, 24(62), pp. 1-76."
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In this paper, we introduce a novel family of iterative algorithms which carry out α\n
-divergence minimisation in a Variational Inference context. They do so by ensuring a systematic decrease at each step in the α\n
-divergence between the variational and the posterior distributions. In its most general form, the variational distribution is a mixture model and our framework allows us to simultaneously optimise the weights and components parameters of this mixture model. Our approach permits us to build on various methods previously proposed for α\n
-divergence minimisation such as Gradient or Power Descent schemes and we also shed a new light on an integrated Expectation Maximization algorithm. Lastly, we provide empirical evidence that our methodology yields improved results on several multimodal target distributions and on a real data example.
"""
"en" => """
In this paper, we introduce a novel family of iterative algorithms which carry out α\n
-divergence minimisation in a Variational Inference context. They do so by ensuring a systematic decrease at each step in the α\n
-divergence between the variational and the posterior distributions. In its most general form, the variational distribution is a mixture model and our framework allows us to simultaneously optimise the weights and components parameters of this mixture model. Our approach permits us to build on various methods previously proposed for α\n
-divergence minimisation such as Gradient or Power Descent schemes and we also shed a new light on an integrated Expectation Maximization algorithm. Lastly, we provide empirical evidence that our methodology yields improved results on several multimodal target distributions and on a real data example.
"""
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2 => Essec\Faculty\Model\Contribution {#2229
#_index: "academ_contributions"
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"slug" => "alpha-divergence-variational-inference-meets-importance-weighted-auto-encoders-methodology-and-asymptotics"
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"title" => "Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics"
"description" => "DAUDEL, K., BENTON, J., SHI, Y. et DOUCET, A. (2023). Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics. <i>Journal of Machine Learning Research</i>, 24(243), pp. 1-83."
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3 => Essec\Faculty\Model\Contribution {#2226
#_index: "academ_contributions"
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"slug" => "mixture-weights-optimisation-for-alpha-divergence-variational-inference"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Mixture weights optimisation for Alpha-Divergence Variational Inference"
"description" => "DAUDEL, K. et DOUC, R. (2021). Mixture weights optimisation for Alpha-Divergence Variational Inference. Dans: <i>35th Conference on Neural Information Processing Systems (NeurIPS 2021)</i>. Curran Associates, Inc. pp. 4397–4408."
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4 => Essec\Faculty\Model\Contribution {#2230
#_index: "academ_contributions"
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"slug" => "alpha-divergence-variational-inference-meets-importance-weighted-auto-encoders-methodology-and-asymptotics"
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"title" => "Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics"
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"docTitle" => "Kamélia DAUDEL"
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