Pierre Ablin
29 papers · 2019–2025 · 5 conferences · across top CS/AI conferences
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Keywords
stochastic optimization
(3)
independent component analysis
(3)
probabilistic modeling
(2)
optimal transport
(2)
residual neural network
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bilevel optimization
(2)
bayesian inference
(1)
feature selection
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transformer architecture
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source separation
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gradient estimation
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sparse representation
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logistic regression
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self-supervised learning
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numerical optimization
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natural language processing
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sample complexity
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automatic differentiation
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attention mechanism
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sparse coding
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Papers
The AdEMAMix Optimizer: Better, Faster, Older
ICLR 2025
Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
ICML 2025
Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
ICML 2025
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
ICML 2025
Theory, Analysis, and Best Practices for Sigmoid Self-Attention
ICLR 2025
Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling
ICLR 2025
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
JMLR 2024
Learning Elastic Costs to Shape Monge Displacements
NIPS 2024
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
AISTATS 2024
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
AISTATS 2024
How Smooth Is Attention?
ICML 2024
Careful with that Scalpel: Improving Gradient Surgery with an EMA
ICML 2024
Optimization without Retraction on the Random Generalized Stiefel Manifold
ICML 2024
How to Scale Your EMA
NIPS 2023
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
ICML 2023
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
NIPS 2022
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
NIPS 2022
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
NIPS 2022
Fast and accurate optimization on the orthogonal manifold without retraction
AISTATS 2022
Sinkformers: Transformers with Doubly Stochastic Attention
AISTATS 2022
Momentum Residual Neural Networks
ICML 2021
Shared Independent Component Analysis for Multi-Subject Neuroimaging
NIPS 2021
mvlearn: Multiview Machine Learning in Python
JMLR 2021
Kernel Stein Discrepancy Descent
ICML 2021
Modeling Shared responses in Neuroimaging Studies through MultiView ICA
NIPS 2020
Super-efficiency of automatic differentiation for functions defined as a minimum
ICML 2020
Stochastic algorithms with descent guarantees for ICA
AISTATS 2019
Learning step sizes for unfolded sparse coding
NIPS 2019
Manifold-regression to predict from MEG/EEG brain signals without source modeling
NIPS 2019