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11/25/2022
High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition
Inferring causal relationships from observational data is a fundamental ...
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04/10/2022
From graphs to DAGs: a low-complexity model and a scalable algorithm
Learning directed acyclic graphs (DAGs) is long known a critical challen...
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11/05/2021
Frugal Machine Learning
Machine learning, already at the core of increasingly many systems and a...
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04/12/2021
Boltzmann Tuning of Generative Models
The paper focuses on the a posteriori tuning of a generative model in or...
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06/24/2020
Distribution-Based Invariant Deep Networks for Learning Meta-Features
Recent advances in deep learning from probability distributions enable t...
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03/04/2020
Variational Auto-Encoder: not all failures are equal
We claim that a source of severe failures for Variational Auto-Encoders ...
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01/22/2020
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
Conditional Generative Models are now acknowledged an essential tool in ...
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07/24/2019
Towards AutoML in the presence of Drift: first results
Research progress in AutoML has lead to state of the art solutions that ...
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06/01/2019
Automated Machine Learning with Monte-Carlo Tree Search (Extended Version)
The AutoML task consists of selecting the proper algorithm in a machine ...
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03/21/2019
Multi-Domain Adversarial Learning
Multi-domain learning (MDL) aims at obtaining a model with minimal avera...
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07/03/2018
New Losses for Generative Adversarial Learning
Generative Adversarial Networks (Goodfellow et al., 2014), a major break...
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03/13/2018
SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning
We present the Structural Agnostic Model (SAM), a framework to estimate ...
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11/24/2017
Causal Generative Neural Networks
We introduce CGNN, a framework to learn functional causal models as gene...
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09/15/2017
Learning Functional Causal Models with Generative Neural Networks
We introduce a new approach to functional causal modeling from observati...
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09/05/2017
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster
When applied to training deep neural networks, stochastic gradient desce...
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09/29/2016
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
This paper addresses the structurally-constrained sparse decomposition o...
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06/10/2014
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely a...
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08/12/2013
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization
This paper investigates the control of an ML component within the Covari...
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04/10/2013
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
This paper proposes a self-adaptation mechanism to manage the resources ...
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03/21/2013
Multi-dimensional sparse structured signal approximation using split Bregman iterations
The paper focuses on the sparse approximation of signals using overcompl...
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07/01/2012
Alternative Restart Strategies for CMA-ES
This paper focuses on the restart strategy of CMA-ES on multi-modal func...
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04/24/2012
Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed
In this paper, we study the performance of IPOP-saACM-ES and BIPOP-saACM...
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04/24/2012
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed
In this paper, we study the performance of IPOP-saACM-ES, recently propo...
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04/11/2012
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
This paper presents a novel mechanism to adapt surrogate-assisted popula...
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10/09/2009