← Authors

Nicolas Flammarion

53 papers · 2015–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (9) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ”¬ Deep Specialist (19) πŸ‘‘ Triple Crown πŸ† Keyword Champion (5) 🀝 Dynamic Duo (15) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (196) ❓ The Questioner (3) ⚑ Prolific Year (10) πŸ’Ž Century Club (53) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter

Conferences

NIPS (19) COLT (10) ICML (9) ICLR (6) JMLR (4) AISTATS (2) AAAI (1) ECCV (1) UAI (1)

Top co-authors

Maksym Andriushchenko (15) Francesco Croce (8) Scott Pesme (7) Etienne Boursier (6) Loucas Pillaud-Vivien (5) Francis Bach (5) Aditya Varre (4) Oğuz Kaan Yüksel (3) Matthias Hein (3) Mathieu Even (3)

Keywords

stochastic gradient descent (14) implicit bia (6) neural network (6) implicit regularization (5) gradient flow (5) diagonal linear network (4) gradient descent (4) convergence rate (3) convex optimization (3) least squares regression (3) relu network (2) adversarial robustness (2) saddle point (2) linear network (2) adversarial attack (2) riemannian manifold (2) sharpness-aware minimization (2) neural network optimization (2) sparse representation (2) stochastic optimization (2)

Papers

On the Sample Complexity of Next-Token Prediction AISTATS 2025
Learning In-context $n$-grams with Transformers: Sub-$n$-grams Are Near-Stationary Points ICML 2025
Learning Parametric Distributions from Samples and Preferences ICML 2025
Learning Algorithms in the Limit COLT 2025
Selective Induction Heads: How Transformers Select Causal Structures in Context ICLR 2025
Simplicity Bias and Optimization Threshold in Two-Layer ReLU Networks ICML 2025
Is In-Context Learning Sufficient for Instruction Following in LLMs? ICLR 2025
Long-Context Linear System Identification ICLR 2025
Early Alignment in Two-Layer Networks Training is a Two-Edged Sword JMLR 2025
Does Refusal Training in LLMs Generalize to the Past Tense? ICLR 2025
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks ICLR 2025
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning ICML 2024
First-order ANIL provably learns representations despite overparametrisation ICLR 2024
Why Do We Need Weight Decay in Modern Deep Learning? NIPS 2024
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models NIPS 2024
SGD vs GD: Rank Deficiency in Linear Networks NIPS 2024
Implicit Bias of Mirror Flow on Separable Data NIPS 2024
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks AISTATS 2024
Linearization Algorithms for Fully Composite Optimization COLT 2023
Sharpness-Aware Minimization Leads to Low-Rank Features NIPS 2023
Quantum Channel Certification with Incoherent Measurements COLT 2023
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability NIPS 2023
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings NIPS 2023
Penalising the biases in norm regularisation enforces sparsity NIPS 2023
On the spectral bias of two-layer linear networks NIPS 2023
Saddle-to-Saddle Dynamics in Diagonal Linear Networks NIPS 2023
A Modern Look at the Relationship between Sharpness and Generalization ICML 2023
SGD with Large Step Sizes Learns Sparse Features ICML 2023
On the effectiveness of adversarial training against common corruptions UAI 2022
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs NIPS 2022
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks AAAI 2022
Trace norm regularization for multi-task learning with scarce data COLT 2022
Accelerated SGD for Non-Strongly-Convex Least Squares COLT 2022
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation COLT 2022
Towards Understanding Sharpness-Aware Minimization ICML 2022
An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. NIPS 2021
Sequential Algorithms for Testing Closeness of Distributions NIPS 2021
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity NIPS 2021
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms NIPS 2021
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent ICML 2020
Square Attack: a query-efficient black-box adversarial attack via random search ECCV 2020
Online Robust Regression via SGD on the l1 loss NIPS 2020
Understanding and Improving Fast Adversarial Training NIPS 2020
Fast Mean Estimation with Sub-Gaussian Rates COLT 2019
Escaping from saddle points on Riemannian manifolds NIPS 2019
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation NIPS 2018
Averaging Stochastic Gradient Descent on Riemannian Manifolds COLT 2018
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017
Robust Discriminative Clustering with Sparse Regularizers JMLR 2017
Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$ COLT 2017
From Averaging to Acceleration, There is Only a Step-size COLT 2015