From Averaging to Acceleration, There is Only a Step-size. - Nicolas Flammarion; Francis R. Bach (COLT, 2015)

Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$. - Nicolas Flammarion; Francis R. Bach (COLT, 2017)

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. - Aymeric Dieuleveut; Nicolas Flammarion; Francis R. Bach (J. Mach. Learn. Res., 2017)

Robust Discriminative Clustering with Sparse Regularizers. - Nicolas Flammarion; Balamurugan Palaniappan; Francis R. Bach (J. Mach. Learn. Res., 2017)

Stochastic Approximation and Least-Squares Regression, with Applications to Machine Learning. (Approximation Stochastique et Régression par Moindres Carrés : Applications en Apprentissage Automatique). - Nicolas Flammarion (2017)

Averaging Stochastic Gradient Descent on Riemannian Manifolds. - Nilesh Tripuraneni; Nicolas Flammarion; Francis R. Bach; Michael I. Jordan (COLT, 2018)

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. - Niladri S. Chatterji; Nicolas Flammarion; Yi-An Ma; Peter L. Bartlett; Michael I. Jordan (ICML, 2018)

Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (NeurIPS, 2018)

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2018)

Sampling Can Be Faster Than Optimization. - Yi-An Ma; Yuansi Chen; Chi Jin 001; Nicolas Flammarion; Michael I. Jordan (CoRR, 2018)

Fast Mean Estimation with Sub-Gaussian Rates. - Yeshwanth Cherapanamjeri; Nicolas Flammarion; Peter L. Bartlett (COLT, 2019)

Escaping from saddle points on Riemannian manifolds. - Yue Sun; Nicolas Flammarion; Maryam Fazel (NeurIPS, 2019)

Is There an Analog of Nesterov Acceleration for MCMC? - Yi-An Ma; Niladri S. Chatterji; Xiang Cheng 006; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. - Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion; Matthias Hein 001 (ECCV (23), 2020)

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. - Scott Pesme; Aymeric Dieuleveut; Nicolas Flammarion (ICML, 2020)

Understanding and Improving Fast Adversarial Training. - Maksym Andriushchenko; Nicolas Flammarion (NeurIPS, 2020)

Online Robust Regression via SGD on the l1 loss. - Scott Pesme; Nicolas Flammarion (NeurIPS, 2020)

Optimal Robust Linear Regression in Nearly Linear Time. - Yeshwanth Cherapanamjeri; Efe Aras; Nilesh Tripuraneni; Michael I. Jordan; Nicolas Flammarion; Peter L. Bartlett (CoRR, 2020)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (CoRR, 2020)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Edoardo Debenedetti; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (NeurIPS Datasets and Benchmarks, 2021)

Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. - Mathieu Even; Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Hadrien Hendrikx; Pierre Gaillard; Laurent Massoulié; Adrien B. Taylor (NeurIPS, 2021)

Sequential Algorithms for Testing Closeness of Distributions. - Aadil Oufkir; Omar Fawzi; Nicolas Flammarion; Aurélien Garivier (NeurIPS, 2021)

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. - Scott Pesme; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2021)

Last iterate convergence of SGD for Least-Squares in the Interpolation regime. - Aditya Vardhan Varre; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2021)

A Continuized View on Nesterov Acceleration. - Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Pierre Gaillard; Adrien B. Taylor (CoRR, 2021)

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. - Mathieu Even; Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Pierre Gaillard; Hadrien Hendrikx; Laurent Massoulié; Adrien B. Taylor (CoRR, 2021)

Linear Speedup in Personalized Collaborative Learning. - El Mahdi Chayti; Sai Praneeth Karimireddy; Sebastian U. Stich; Nicolas Flammarion; Martin Jaggi (CoRR, 2021)

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. - Francesco Croce; Maksym Andriushchenko; Naman D. Singh; Nicolas Flammarion; Matthias Hein 001 (AAAI, 2022)

Trace norm regularization for multi-task learning with scarce data. - Etienne Boursier; Mikhail Konobeev; Nicolas Flammarion (COLT, 2022)

Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. - Loucas Pillaud-Vivien; Julien Reygner; Nicolas Flammarion (COLT, 2022)

Accelerated SGD for Non-Strongly-Convex Least Squares. - Aditya Varre; Nicolas Flammarion (COLT, 2022)

ARIA: Adversarially Robust Image Attribution for Content Provenance. - Maksym Andriushchenko; Xiaoyang Rebecca Li; Geoffrey Oxholm; Thomas Gittings; Tu Bui; Nicolas Flammarion; John P. Collomosse (CVPR Workshops, 2022)

Towards Understanding Sharpness-Aware Minimization. - Maksym Andriushchenko; Nicolas Flammarion (ICML, 2022)

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. - Etienne Boursier; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2022)

On the effectiveness of adversarial training against common corruptions. - Klim Kireev; Maksym Andriushchenko; Nicolas Flammarion (UAI, 2022)

Sequential algorithms for testing identity and closeness of distributions. - Omar Fawzi; Nicolas Flammarion; Aurélien Garivier; Aadil Oufkir (CoRR, 2022)

An Efficient Sampling Algorithm for Non-smooth Composite Potentials. - Wenlong Mou; Nicolas Flammarion; Martin J. Wainwright; Peter L. Bartlett (J. Mach. Learn. Res., 2022)

Quantum Channel Certification with Incoherent Measurements. - Omar Fawzi; Nicolas Flammarion; Aurélien Garivier; Aadil Oufkir (COLT, 2023)

Linearization Algorithms for Fully Composite Optimization. - Maria-Luiza Vladarean; Nikita Doikov; Martin Jaggi; Nicolas Flammarion (COLT, 2023)

A Modern Look at the Relationship between Sharpness and Generalization. - Maksym Andriushchenko; Francesco Croce; Maximilian Müller; Matthias Hein 001; Nicolas Flammarion (ICML, 2023)

SGD with Large Step Sizes Learns Sparse Features. - Maksym Andriushchenko; Aditya Vardhan Varre; Loucas Pillaud-Vivien; Nicolas Flammarion (ICML, 2023)

Sharpness-Aware Minimization Leads to Low-Rank Features. - Maksym Andriushchenko; Dara Bahri; Hossein Mobahi; Nicolas Flammarion (NeurIPS, 2023)

Penalising the biases in norm regularisation enforces sparsity. - Etienne Boursier; Nicolas Flammarion (NeurIPS, 2023)

(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability. - Mathieu Even; Scott Pesme; Suriya Gunasekar; Nicolas Flammarion (NeurIPS, 2023)

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. - Klim Kireev; Maksym Andriushchenko; Carmela Troncoso; Nicolas Flammarion (NeurIPS, 2023)

Saddle-to-Saddle Dynamics in Diagonal Linear Networks. - Scott Pesme; Nicolas Flammarion (NeurIPS, 2023)

On the spectral bias of two-layer linear networks. - Aditya Vardhan Varre; Maria-Luiza Vladarean; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2023)

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability. - Mathieu Even; Scott Pesme; Suriya Gunasekar; Nicolas Flammarion (CoRR, 2023)

Quantum Channel Certification with Incoherent Strategies. - Omar Fawzi; Nicolas Flammarion; Aurélien Garivier; Aadil Oufkir (CoRR, 2023)

Model agnostic methods meta-learn despite misspecifications. - Yüksel Oguz; Etienne Boursier; Nicolas Flammarion (CoRR, 2023)

On Adaptivity in Quantum Testing. - Omar Fawzi; Nicolas Flammarion; Aurélien Garivier; Aadil Oufkir (Trans. Mach. Learn. Res., 2023)

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. - Hristo Papazov; Scott Pesme; Nicolas Flammarion (AISTATS, 2024)

First-order ANIL provably learns representations despite overparametrisation. - Oguz Kaan Yüksel; Etienne Boursier; Nicolas Flammarion (ICLR, 2024)

Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning. - Hao Zhao; Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion (ICML, 2024)

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. - Patrick Chao; Edoardo Debenedetti; Alexander Robey; Maksym Andriushchenko; Francesco Croce; Vikash Sehwag; Edgar Dobriban; Nicolas Flammarion; George J. Pappas; Florian Tramèr; Hamed Hassani; Eric Wong 001 (NeurIPS, 2024)

Why Do We Need Weight Decay in Modern Deep Learning? - Francesco D'Angelo; Maksym Andriushchenko; Aditya Vardhan Varre; Nicolas Flammarion (NeurIPS, 2024)

Implicit Bias of Mirror Flow on Separable Data. - Scott Pesme; Radu-Alexandru Dragomir; Nicolas Flammarion (NeurIPS, 2024)

SGD vs GD: Rank Deficiency in Linear Networks. - Aditya Vardhan Varre; Margarita Sagitova; Nicolas Flammarion (NeurIPS, 2024)

Early alignment in two-layer networks training is a two-edged sword. - Etienne Boursier; Nicolas Flammarion (CoRR, 2024)

Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. - Javier Rando; Francesco Croce; Krystof Mitka; Stepan Shabalin; Maksym Andriushchenko; Nicolas Flammarion; Florian Tramèr (CoRR, 2024)

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. - Beatriz Borges; Negar Foroutan; Deniz Bayazit; Anna Sotnikova; Syrielle Montariol; Tanya Nazaretzky; Mohammadreza Banaei; Alireza Sakhaeirad; Philippe Servant; Seyed Parsa Neshaei; Jibril Frej; Angelika Romanou; et al. (CoRR, 2024)

Simplicity bias and optimization threshold in two-layer ReLU networks. - Etienne Boursier; Nicolas Flammarion (CoRR, 2024)

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics. - Sara Ghazanfari; Siddharth Garg; Nicolas Flammarion; Prashanth Krishnamurthy; Farshad Khorrami; Francesco Croce (CoRR, 2024)

On the Sample Complexity of Next-Token Prediction. - Oguz Kaan Yüksel; Nicolas Flammarion (AISTATS, 2025)

Learning Algorithms in the Limit. - Hristo Papazov; Nicolas Flammarion (COLT, 2025)

Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. - Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion (ICLR, 2025)

Does Refusal Training in LLMs Generalize to the Past Tense? - Maksym Andriushchenko; Nicolas Flammarion (ICLR, 2025)

Selective induction Heads: How Transformers Select Causal Structures in Context. - Francesco D'Angelo; Francesco Croce; Nicolas Flammarion (ICLR, 2025)

Long-Context Linear System Identification. - Oguz Kaan Yüksel; Mathieu Even; Nicolas Flammarion (ICLR, 2025)

Is In-Context Learning Sufficient for Instruction Following in LLMs? - Hao Zhao; Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion (ICLR, 2025)

Learning Parametric Distributions from Samples and Preferences. - Marc Jourdan; Gizem Yüce; Nicolas Flammarion (CoRR, 2025)

Chain-of-Frames: Advancing Video Understanding in Multimodal LLMs via Frame-Aware Reasoning. - Sara Ghazanfari; Francesco Croce; Nicolas Flammarion; Prashanth Krishnamurthy; Farshad Khorrami; Siddharth Garg (CoRR, 2025)

FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens. - Christian Schlarmann; Francesco Croce; Nicolas Flammarion; Matthias Hein 001 (CoRR, 2025)

OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents. - Thomas Kuntz; Agatha Duzan; Hao Zhao; Francesco Croce; Zico Kolter; Nicolas Flammarion; Maksym Andriushchenko (CoRR, 2025)

Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points. - Aditya Varre; Gizem Yüce; Nicolas Flammarion (CoRR, 2025)