Search Results for author:
Found 20 papers, 10 papers with code
Can local explanation techniques explain linear additive models?
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores.
Knowledge-Guided Additive Modeling For Supervised Regression
Learning processes by exploiting restricted domain knowledge is an important task across a plethora of scientific areas, with more and more hybrid methods combining data-driven and model-based approaches.
Distillation from heterogeneous unlabeled collections
Compressing deep networks is essential to expand their range of applications to constrained settings.
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-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output.
On The Transferability of Deep-Q Networks
Transfer Learning (TL) is an efficient machine learning paradigm that allows overcoming some of the hurdles that characterize the successful training of deep neural networks, ranging from long training times to the needs of large datasets.
Optimizing model-agnostic Random Subspace ensembles
The degree of randomization in our parametric Random Subspace is thus automatically tuned through the optimization of the feature selection probabilities.
Evaluating Local Explanations using White-box Models
Evaluating explanation techniques using human subjects is costly, time-consuming and can lead to subjectivity in the assessments.
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 multi-agent reinforcement learning (MARL) problems occurring in cooperative settings.
On the Transferability of Winning Tickets in Non-Natural Image Datasets
We study the generalization properties of pruned neural networks that are the winners of the lottery ticket hypothesis on datasets of natural images.
Multi-task pre-training of deep neural networks for digital pathology
In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology.
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 \textit{model-free} Deep Reinforcement Learning (DRL) algorithms.
Gradient tree boosting with random output projections for multi-label classification and multi-output regression
We consider several extensions of gradient boosting to address such problems.
Deep Quality-Value (DQV) Learning
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning.
Random Subspace with Trees for Feature Selection Under Memory Constraints
Dealing with datasets of very high dimension is a major challenge in machine learning.
Globally Induced Forest: A Prepruning Compression Scheme
Tree-based ensemble models are heavy memory-wise.
Context-dependent feature analysis with random forests
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output.
Simple connectome inference from partial correlation statistics in calcium imaging
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data.
Classifying pairs with trees for supervised biological network inference
Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference.
Random forests with random projections of the output space for high dimensional multi-label classification
We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification.
Understanding variable importances in forests of randomized trees
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view.
