Search Results for author:
Found 23 papers, 7 papers with code
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
In the context of linear structural equation models (SEMs), this paper focuses on learning causal structures from the inverse covariance matrix.
From graphs to DAGs: a low-complexity model and a scalable algorithm
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling.
Frugal Machine Learning
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.
Distribution-Based Invariant Deep Networks for Learning Meta-Features
On both tasks, Dida learns meta-features supporting the characterization of a (labelled) dataset.
Variational Auto-Encoder: not all failures are equal
We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distribution class used for the observation model. A first theoretical and experimental contribution of the paper is to establish that even in the large sample limit with arbitrarily powerful neural architectures and latent space, the VAE failsif the sharpness of the distribution class does not match the scale of the data. Our second claim is that the distribution sharpness must preferably be learned by the VAE (as opposed to, fixed and optimized offline): Autonomously adjusting this sharpness allows the VAE to dynamically control the trade-off between the optimization of the reconstruction loss and the latent compression.
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
This paper focuses on their control.
Automated Machine Learning with Monte-Carlo Tree Search
The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand.
Analysis of the AutoML Challenge Series 2015–2018
The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn.
Ranked #1 on
AutoML
on Chalearn-AutoML-1
Locally Linear Unsupervised Feature Selection
The paper, interested in unsupervised feature selection, aims to retain the features best accounting for the local patterns in the data.
New Losses for Generative Adversarial Learning
Generative Adversarial Networks (Goodfellow et al., 2014), a major breakthrough in the field of generative modeling, learn a discriminator to estimate some distance between the target and the candidate distributions.
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.
Causal Generative Neural Networks
We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.
Learning Functional Causal Models with Generative Neural Networks
We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN).
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster
When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and gradient norm explode.
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries.
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems.
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits
Motivated by applications in energy management, this paper presents the Multi-Armed Risk-Aware Bandit (MARAB) algorithm.
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization
This weakness is commonly addressed through surrogate optimization, learning an estimate of the objective function a. k. a.
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions.
Multi-dimensional sparse structured signal approximation using split Bregman iterations
An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features.
APRIL: Active Preference-learning based Reinforcement Learning
This paper focuses on reinforcement learning (RL) with limited prior knowledge.
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters.
Feature Selection as a One-Player Game
This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy.
