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11/10/2022
Regression as Classification: Influence of Task Formulation on Neural Network Features
Neural networks can be trained to solve regression problems by using gra...
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06/14/2022
Scaling ResNets in the Large-depth Regime
Deep ResNets are recognized for achieving state-of-the-art results in co...
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06/02/2021
Framing RNN as a kernel method: A neural ODE approach
Building on the interpretation of a recurrent neural network (RNN) as a ...
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05/31/2021
Efficient and Modular Implicit Differentiation
Automatic differentiation (autodiff) has revolutionized machine learning...
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11/16/2020
Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
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10/16/2020
Differentiable Divergences Between Time Series
Computing the discrepancy between time series of variable sizes is notor...
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06/10/2020
On Mixup Regularization
Mixup is a data augmentation technique that creates new examples as conv...
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04/26/2020
Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
When test resources are scarce, a viable alternative to test for the pre...
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02/25/2020
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from obs...
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02/20/2020
Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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02/08/2020
Supervised Quantile Normalization for Low-rank Matrix Approximation
Low rank matrix factorization is a fundamental building block in machine...
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10/20/2019
Differentiable Deep Clustering with Cluster Size Constraints
Clustering is a fundamental unsupervised learning approach. Many cluster...
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09/21/2019
ASNI: Adaptive Structured Noise Injection for shallow and deep neural networks
Dropout is a regularisation technique in neural network training where u...
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05/30/2019
Deep multi-class learning from label proportions
We propose a learning algorithm capable of learning from label proportio...
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05/28/2019
Differentiable Sorting using Optimal Transport:The Sinkhorn CDF and Quantile Operator
Sorting an array is a fundamental routine in machine learning, one that ...
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05/21/2018
Relating Leverage Scores and Density using Regularized Christoffel Functions
Statistical leverage scores emerged as a fundamental tool for matrix ske...
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02/26/2018
DropLasso: A robust variant of Lasso for single cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to mea...
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02/23/2018
The Weighted Kendall and High-order Kernels for Permutations
We propose new positive definite kernels for permutations. First we intr...
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02/16/2018
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Learning sparse linear models with two-way interactions is desirable in ...
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06/01/2017
Supervised Quantile Normalisation
Quantile normalisation is a popular normalisation method for data subjec...
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06/24/2015
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
Microbial identification is a central issue in microbiology, in particul...
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05/26/2015
Large-scale Machine Learning for Metagenomics Sequence Classification
Metagenomics characterizes the taxonomic diversity of microbial communit...
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07/19/2014
Tight convex relaxations for sparse matrix factorization
Based on a new atomic norm, we propose a new convex formulation for spar...
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05/12/2014
Consistency of random forests
Random forests are a learning algorithm proposed by Breiman [Mach. Learn...
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05/06/2012
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
Inferring the structure of gene regulatory networks (GRN) from gene expr...
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10/03/2011
Group Lasso with Overlaps: the Latent Group Lasso approach
We study a norm for structured sparsity which leads to sparse linear pre...
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06/21/2011
The group fused Lasso for multiple change-point detection
We present the group fused Lasso for detection of multiple change-points...
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06/01/2011
ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples
Elucidating the genetic basis of human diseases is a central goal of gen...
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01/26/2011
The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures
Motivation: Biomarker discovery from high-dimensional data is a crucial ...
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10/05/2010
A bagging SVM to learn from positive and unlabeled examples
We consider the problem of learning a binary classifier from a training ...
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04/28/2010
Many-to-Many Graph Matching: a Continuous Relaxation Approach
Graphs provide an efficient tool for object representation in various co...
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01/18/2010
Increasing stability and interpretability of gene expression signatures
Motivation : Molecular signatures for diagnosis or prognosis estimated f...
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01/23/2008
A path following algorithm for the graph matching problem
We propose a convex-concave programming approach for the labeled weighte...
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10/06/2006