← Authors

Mathieu Blondel

36 papers · 2011–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (3) 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (14) πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌱 Topic Pioneer πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (3) πŸ—ƒοΈ Keyword Collector (126) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ’Ž Century Club (36) πŸ”₯ Unstoppable (10) πŸ“ˆ Trend Setter ❓ The Questioner

Conferences

ICML (14) NIPS (9) AISTATS (6) JMLR (4) ICLR (2) IJCAI (1)

Top co-authors

Vlad Niculae (6) Gabriel PeyrΓ© (5) Quentin Berthet (5) Michael Eli Sander (4) Naonori Ueda (4) Felipe Llinares-LΓ³pez (4) Tianlin Liu (4) Alexandre Gramfort (3) Fabian Pedregosa (3) Marco Cuturi (3)

Keywords

convex optimization (8) structured prediction (5) fenchel-young loss (5) attention mechanism (4) hyperparameter optimization (3) loss function (3) factorization machine (3) ordinal regression (3) implicit differentiation (3) differentiable programming (3) feature interaction (2) natural language processing (2) optimal transport (2) sinkhorn algorithm (2) high-dimensional datum (2) logistic loss (2) multilabel classification (2) dynamic time warping (2) natural language inference (2) sparse optimization (2)

Papers

Loss Functions and Operators Generated by f-Divergences ICML 2025
Joint Learning of Energy-based Models and their Partition Function ICML 2025
On Teacher Hacking in Language Model Distillation ICML 2025
Implicit Diffusion: Efficient optimization through stochastic sampling AISTATS 2025
Learning with Fitzpatrick Losses NIPS 2024
How do Transformers Perform In-Context Autoregressive Learning ? ICML 2024
Decoding-time Realignment of Language Models ICML 2024
Stepping on the Edge: Curvature Aware Learning Rate Tuners NIPS 2024
Sparsity-Constrained Optimal Transport ICLR 2023
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective ICML 2023
Sinkformers: Transformers with Doubly Stochastic Attention AISTATS 2022
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning JMLR 2022
Efficient and Modular Implicit Differentiation NIPS 2022
Sparse Continuous Distributions and Fenchel-Young Losses JMLR 2022
Learning Energy Networks with Generalized Fenchel-Young Losses NIPS 2022
Differentiable Divergences Between Time Series AISTATS 2021
Momentum Residual Neural Networks ICML 2021
Learning with Differentiable Pertubed Optimizers NIPS 2020
Learning with Fenchel-Young losses JMLR 2020
Implicit differentiation of Lasso-type models for hyperparameter optimization ICML 2020
Fast Differentiable Sorting and Ranking ICML 2020
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms AISTATS 2019
Structured Prediction with Projection Oracles NIPS 2019
Geometric Losses for Distributional Learning ICML 2019
SparseMAP: Differentiable Sparse Structured Inference ICML 2018
Large Scale Optimal Transport and Mapping Estimation ICLR 2018
Smooth and Sparse Optimal Transport AISTATS 2018
Differentiable Dynamic Programming for Structured Prediction and Attention ICML 2018
Soft-DTW: a Differentiable Loss Function for Time-Series ICML 2017
A Regularized Framework for Sparse and Structured Neural Attention NIPS 2017
Multi-output Polynomial Networks and Factorization Machines NIPS 2017
SVD-Based Screening for the Graphical Lasso IJCAI 2017
Higher-Order Factorization Machines NIPS 2016
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms ICML 2016
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion AISTATS 2014
Scikit-learn: Machine Learning in Python JMLR 2011