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07/05/2023
Knowledge-Guided Additive Modeling For Supervised Regression
Learning processes by exploiting restricted domain knowledge is an impor...
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01/17/2022
Distillation from heterogeneous unlabeled collections
Compressing deep networks is essential to expand their range of applicat...
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11/03/2021
From global to local MDI variable importances for random forests and when they are Shapley values
Random forests have been widely used for their ability to provide so-cal...
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10/06/2021
On The Transferability of Deep-Q Networks
Transfer Learning (TL) is an efficient machine learning paradigm that al...
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09/07/2021
Optimizing model-agnostic Random Subspace ensembles
This paper presents a model-agnostic ensemble approach for supervised le...
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06/04/2021
Evaluation of Local Model-Agnostic Explanations Using Ground Truth
Explanation techniques are commonly evaluated using human-grounded metho...
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12/22/2020
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
This paper introduces four new algorithms that can be used for tackling ...
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05/11/2020
On the Transferability of Winning Tickets in Non-Natural Image Datasets
We study the generalization properties of pruned neural networks that ar...
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05/05/2020
Multi-task pre-training of deep neural networks for digital pathology
In this work, we investigate multi-task learning as a way of pre-trainin...
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05/05/2020
Multi-task pre-training of deep neural networks
In this work, we investigate multi-task learning as a way of pre-trainin...
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09/01/2019
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
This paper makes one step forward towards characterizing a new family of...
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05/18/2019
Gradient tree boosting with random output projections for multi-label classification and multi-output regression
In many applications of supervised learning, multiple classification or ...
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09/30/2018
Deep Quality-Value (DQV) Learning
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called ...
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09/04/2017
Random Subspace with Trees for Feature Selection Under Memory Constraints
Dealing with datasets of very high dimension is a major challenge in mac...
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05/12/2016
Context-dependent feature analysis with random forests
In many cases, feature selection is often more complicated than identify...
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06/30/2014
Simple connectome inference from partial correlation statistics in calcium imaging
In this work, we propose a simple yet effective solution to the problem ...
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04/24/2014
Classifying pairs with trees for supervised biological network inference
Networks are ubiquitous in biology and computational approaches have bee...
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04/14/2014