← Authors

Aurelien Lucchi

53 papers · 2013–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (9) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (12) πŸ”¬ Deep Specialist (23) πŸ† Grand Slam 🀝 Dynamic Duo (20) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (209) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ’Ž Century Club (53) πŸ”₯ Unstoppable (11) πŸ“ˆ Trend Setter

Conferences

NIPS (16) ICML (13) AISTATS (11) ICLR (5) ICCV (3) AAAI (1) CVPR (1) ECCV (1) SEMEVAL (1) UAI (1)

Top co-authors

Thomas Hofmann (20) Antonio Orvieto (16) Hadi Daneshmand (6) Jonas KΓΆhler (6) Dario Pavllo (5) Luca Biggio (4) Enea Monzio Compagnoni (4) Hans Kersting (4) Tin Sum Cheng (3) Rustem Islamov (3)

Keywords

stochastic gradient descent (8) gradient descent (7) stochastic optimization (7) generative adversarial network (4) non-convex optimization (4) variance reduction (4) saddle point (3) stochastic differential equation (3) learning curve (2) derivative-free optimization (2) convergence rate (2) convergence guarantee (2) kernel ridge regression (2) adaptive sampling (2) batch normalization (2) global convergence (2) convergence analysis (2) neural network optimization (2) minimax optimization (2) hessian matrix (2)

Papers

Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise ICLR 2025
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs AISTATS 2025
Cubic regularized subspace Newton for non-convex optimization AISTATS 2025
Loss Landscape Characterization of Neural Networks without Over-Parametrization NIPS 2024
Initial Guessing Bias: How Untrained Networks Favor Some Classes ICML 2024
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression NIPS 2024
Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks NIPS 2024
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum ICML 2024
SDEs for Minimax Optimization AISTATS 2024
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers NIPS 2023
An SDE for Modeling SAM: Theory and Insights ICML 2023
A Theoretical Analysis of the Learning Dynamics under Class Imbalance ICML 2023
Mastering Spatial Graph Prediction of Road Networks ICCV 2023
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression NIPS 2023
On the Theoretical Properties of Noise Correlation in Stochastic Optimization NIPS 2022
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse NIPS 2022
Vanishing Curvature in Randomly Initialized Deep ReLU Networks AISTATS 2022
Phenomenology of Double Descent in Finite-Width Neural Networks ICLR 2022
Generalization Through the Lens of Leave-One-Out Error ICLR 2022
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning AISTATS 2022
Anticorrelated Noise Injection for Improved Generalization ICML 2022
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity AISTATS 2022
Neural Symbolic Regression that scales ICML 2021
Scalable Graph Networks for Particle Simulations AAAI 2021
On the Second-order Convergence Properties of Random Search Methods NIPS 2021
Learning Generative Models of Textured 3D Meshes From Real-World Images ICCV 2021
Momentum Improves Optimization on Riemannian Manifolds AISTATS 2021
Direct-Search for a Class of Stochastic Min-Max Problems AISTATS 2021
Convolutional Generation of Textured 3D Meshes NIPS 2020
Batch normalization provably avoids ranks collapse for randomly initialised deep networks NIPS 2020
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization AISTATS 2020
Controlling Style and Semantics in Weakly-Supervised Image Generation ECCV 2020
An Accelerated DFO Algorithm for Finite-sum Convex Functions ICML 2020
Randomized Block-Diagonal Preconditioning for Parallel Learning ICML 2020
Topological Map Extraction From Overhead Images ICCV 2019
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization AISTATS 2019
The Role of Memory in Stochastic Optimization UAI 2019
Local Saddle Point Optimization: A Curvature Exploitation Approach AISTATS 2019
A Domain Agnostic Measure for Monitoring and Evaluating GANs NIPS 2019
Shadowing Properties of Optimization Algorithms NIPS 2019
Continuous-time Models for Stochastic Optimization Algorithms NIPS 2019
Semantic Interpolation in Implicit Models ICLR 2018
Escaping Saddles with Stochastic Gradients ICML 2018
A Distributed Second-Order Algorithm You Can Trust ICML 2018
An Online Learning Approach to Generative Adversarial Networks ICLR 2018
Sub-sampled Cubic Regularization for Non-convex Optimization ICML 2017
Stabilizing Training of Generative Adversarial Networks through Regularization NIPS 2017
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision SEMEVAL 2016
Starting Small - Learning with Adaptive Sample Sizes ICML 2016
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy NIPS 2016
Variance Reduced Stochastic Gradient Descent with Neighbors NIPS 2015
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets CVPR 2013
An Optimal Policy for Target Localization with Application to Electron Microscopy ICML 2013