Mathieu Blondel
36 papers · 2011–2025 · 6 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (17) π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge π£ Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(17)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π±
Topic Pioneer
π¬
Deep Specialist
(10)
π
Keyword Champion
(3)
ποΈ
Keyword Collector
(126)
β‘
Prolific Year
(5)
π
Conference Pioneer
π
Century Club
(36)
π₯
Unstoppable
(10)
π
Trend Setter
β
The Questioner
Conferences
ICML (14)
NIPS (9)
AISTATS (6)
JMLR (4)
ICLR (2)
IJCAI (1)
Top co-authors
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