Jean‐Philippe Vert

1. Learning with Differentiable Perturbed Optimizers
   2020/02/20 by Quentin Berthet, Berthet, Quentin, Mathieu Blondel +10 · 4 voices · 10 citations
   Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Metaheuristic Optimization Algorithms Research #Neural Networks and Applications #cs.LG #math.OC #stat.ML
2. Efficient and Modular Implicit Differentiation
   2021/05/31 by Mathieu Blondel, Quentin Berthet, Blondel, Mathieu +13 · 52 citations
   Engineering · Mathematics · Physics and Astronomy · #Advanced Control Systems Optimization #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
3. Differentiable Ranks and Sorting using Optimal Transport
   2019/05/28 by Marco Cuturi, Cuturi, Marco, Olivier Teboul +3 · 13 citations
   Computer Science · #Machine Learning and Algorithms #Machine Learning and Data Classification #Bayesian Methods and Mixture Models
4. A path following algorithm for the graph matching problem
   2008/01/23 by Mikhail Zaslavskiy, Zaslavskiy, Mikhail, Francis Bach +4 · 8 citations
   Computer Science · Decision Sciences · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Data Quality and Management #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #Graph Theory and Algorithms #cs.CV #cs.DM
5. A bagging SVM to learn from positive and unlabeled examples
   2010/10/05 by Fantine Mordelet, Mordelet, Fantine, Jean-Philippe Vert +2 · 7 citations
   Computer Science · Mathematics · #Imbalanced Data Classification Techniques #Machine Learning and Algorithms #Machine Learning and Data Classification #stat.ML
6. Group Lasso with Overlaps: the Latent Group Lasso approach
   2011/10/03 by Guillaume Obozinski, Laurent Jacob, Obozinski, Guillaume +3 · 7 citations
   Mathematics · Medicine · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Systemic Lupus Erythematosus Research
7. Regression as Classification: Influence of Task Formulation on Neural Network Features
   2022/11/10 by Lawrence Stewart, Stewart, Lawrence, Francis Bach +5 · 7 citations
   Computer Science · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)
8. A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
   2008/02/11 by Jacob Abernethy, Francis Bach, Abernethy, Jacob +6 · 4 citations
   Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques #cs.LG
9. On Mixup Regularization
   2020/06/10 by Luigi Carratino, Carratino, Luigi, Moustapha Cissé +5 · 5 citations
   Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification
10. Clustered Multi-Task Learning: A Convex Formulation
    2008/09/11 by Laurent Jacob, Jacob, Laurent, Francis Bach +4 · 3 citations
    Computer Science · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning and ELM #cs.LG
11. Relating Leverage Scores and Density using Regularized Christoffel Functions
    2018/05/21 by Edouard Pauwels, Pauwels, Edouard, Francis R. Bach +4 · 4 citations
    Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques
12. Tight convex relaxations for sparse matrix factorization
    2014/07/19 by Émile Richard, Richard, Emile, Guillaume Obozinski +3 · 3 citations
    Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Blind Source Separation Techniques #Matrix Theory and Algorithms
13. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
    2012/05/06 by Anne-Claire Haury, Fantine Mordelet, Haury, Anne-Claire +5 · 2 citations
    Biochemistry, Genetics and Molecular Biology · #CRISPR and Genetic Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Machine Learning (stat.ML) #Molecular Biology Techniques and Applications #Quantitative Methods (q-bio.QM)
14. The group fused Lasso for multiple change-point detection
    2011/06/21 by Kevin Bleakley, Bleakley, Kevin, Jean‐Philippe Vert +1 · 2 citations
    Mathematics · #Advanced Statistical Methods and Models #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #Statistical Methods and Inference
15. Differentiable Divergences Between Time Series
    2020/10/16 by Mathieu Blondel, Blondel, Mathieu, Arthur Mensch +3 · 2 citations
    Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Time Series Analysis and Forecasting
16. DropLasso: A robust variant of Lasso for single cell RNA-seq data
    2018/02/26 by Beyrem Khalfaoui, Jean‐Philippe Vert, Khalfaoui, Beyrem +1 · 1 citation
    Biochemistry, Genetics and Molecular Biology · #Cancer-related molecular mechanisms research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics
17. Differentiable Deep Clustering with Cluster Size Constraints
    2019/10/20 by Aude Genevay, Genevay, Aude, Gabriel Dulac-Arnold +3 · 1 citation
    Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Surveillance and Tracking Methods
18. Framing RNN as a kernel method: A neural ODE approach
    2021/06/02 by Adeline Fermanian, Fermanian, Adeline, Pierre Marion +5 · 1 citation
    Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications
19. Scaling ResNets in the Large-depth Regime
    2022/06/14 by Pierre Marion, Adeline Fermanian, Marion, Pierre +5 · 1 citation
    Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications
20. WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
    2018/02/16 by Marine Le Morvan, Jean‐Philippe Vert, Morvan, Marine Le +1 · 1 citation
    Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Genomics and Chromatin Dynamics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM)