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05/25/2023
Exponential Smoothing for Off-Policy Learning
Off-policy learning (OPL) aims at finding improved policies from logged ...
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10/24/2022
Sampling with Mollified Interaction Energy Descent
Sampling from a target measure whose density is only known up to a norma...
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07/08/2022
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study
This paper studies the Variational Inference (VI) used for training Baye...
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06/17/2022
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
Many problems in machine learning can be formulated as optimizing a conv...
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10/29/2021
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff
Adaptive importance sampling is a widely spread Monte Carlo technique th...
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05/20/2021
Kernel Stein Discrepancy Descent
Among dissimilarities between probability distributions, the Kernel Stei...
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06/17/2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which ...
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02/07/2020
Wasserstein Proximal Gradient
We consider the task of sampling from a log-concave probability distribu...
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06/11/2019
Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy...
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10/15/2018
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Whereas most dimensionality reduction techniques (e.g. PCA, ICA, NMF) fo...
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07/06/2018
A Structured Prediction Approach for Label Ranking
We propose to solve a label ranking problem as a structured output regre...
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10/31/2017