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Gabriele Abbati

3 accepted papers

2019

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

ICML 2019oral

Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy…

2019

On the Fairness of Disentangled Representations

NeurIPS 2019poster

Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for i…

Cited by 267SourcePDFScholar
2018

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling

AISTATS 2018poster

Gradient-based optimization and Markov Chain Monte Carlo sampling can be found at the heart of several machine learning methods. In high-dimensional settings, well-known issues such as slow-mixing, non-convexity and correlations can hinder the algorithms’ efficiency. In order to overcome these diffi…