Lucchi, Aurelien

57 publications

ICLR 2025 Adaptive Methods Through the Lens of SDEs: Theoretical Insights on the Role of Noise Enea Monzio Compagnoni, Tianlin Liu, Rustem Islamov, Frank Norbert Proske, Antonio Orvieto, Aurelien Lucchi

AISTATS 2025 Cubic Regularized Subspace Newton for Non-Convex Optimization Jim Zhao, Nikita Doikov, Aurelien Lucchi

NeurIPS 2025 Enhancing Optimizer Stability: Momentum Adaptation of the NGN Step-Size Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurelien Lucchi

TMLR 2025 Optimization Guarantees for Square-Root Natural-Gradient Variational Inference Navish Kumar, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Aurelien Lucchi

AISTATS 2025 Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske, Aurelien Lucchi

NeurIPS 2024 A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression Tin Sum Cheng, Aurelien Lucchi, Anastasis Kratsios, David Belius

ICML 2024 Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum Tin Sum Cheng, Aurelien Lucchi, Anastasis Kratsios, David Belius

ICML 2024 Initial Guessing Bias: How Untrained Networks Favor Some Classes Emanuele Francazi, Aurelien Lucchi, Marco Baity-Jesi

NeurIPS 2024 Loss Landscape Characterization of Neural Networks Without Over-Parametrization Rustem Islamov, Niccoló Ajroldi, Antonio Orvieto, Aurelien Lucchi

AISTATS 2024 SDEs for Minimax Optimization Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurelien Lucchi

NeurIPS 2024 Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks Jim Zhao, Sidak Pal Singh, Aurelien Lucchi

ICML 2023 A Theoretical Analysis of the Learning Dynamics Under Class Imbalance Emanuele Francazi, Marco Baity-Jesi, Aurelien Lucchi

NeurIPS 2023 A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression Tin Sum Cheng, Aurelien Lucchi, Anastasis Kratsios, Ivan Dokmanić, David Belius

ICML 2023 An SDE for Modeling SAM: Theory and Insights Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurelien Lucchi

NeurIPS 2023 Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurelien Lucchi, Thomas Hofmann

ICCV 2023 Mastering Spatial Graph Prediction of Road Networks Anagnostidis Sotiris, Aurelien Lucchi, Thomas Hofmann

AISTATS 2022 A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning Youssef Diouane, Aurelien Lucchi, Vihang Prakash Patil

AISTATS 2022 Faster Single-Loop Algorithms for Minimax Optimization Without Strong Concavity Junchi Yang, Antonio Orvieto, Aurelien Lucchi, Niao He

AISTATS 2022 Vanishing Curvature in Randomly Initialized Deep ReLU Networks Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurelien Lucchi

ICML 2022 Anticorrelated Noise Injection for Improved Generalization Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, Aurelien Lucchi

NeurIPSW 2022 Batch Size Selection by Stochastic Optimal Control Jim Zhao, Aurelien Lucchi, Frank Norbert Proske, Antonio Orvieto, Hans Kersting

ICLR 2022 Generalization Through the Lens of Leave-One-Out Error Gregor Bachmann, Thomas Hofmann, Aurelien Lucchi

NeurIPS 2022 On the Theoretical Properties of Noise Correlation in Stochastic Optimization Aurelien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting

ICLR 2022 Phenomenology of Double Descent in Finite-Width Neural Networks Sidak Pal Singh, Aurelien Lucchi, Thomas Hofmann, Bernhard Schölkopf

NeurIPS 2022 Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurelien Lucchi

AISTATS 2021 Direct-Search for a Class of Stochastic Min-Max Problems Sotirios-Konstantinos Anagnostidis, Aurelien Lucchi, Youssef Diouane

AISTATS 2021 Momentum Improves Optimization on Riemannian Manifolds Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi

ICCV 2021 Learning Generative Models of Textured 3D Meshes from Real-World Images Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurelien Lucchi

ICML 2021 Neural Symbolic Regression That Scales Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo

NeurIPS 2021 On the Second-Order Convergence Properties of Random Search Methods Aurelien Lucchi, Antonio Orvieto, Adamos Solomou

AAAI 2021 Scalable Graph Networks for Particle Simulations Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin

AISTATS 2020 A Continuous-Time Perspective for Modeling Acceleration in Riemannian Optimization Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi

NeurIPSW 2020 A Seq2Seq Approach to Symbolic Regression Luca Biggio, Tommaso Bendinelli, Aurelien Lucchi, Giambattista Parascandolo

ICML 2020 An Accelerated DFO Algorithm for Finite-Sum Convex Functions Yuwen Chen, Antonio Orvieto, Aurelien Lucchi

NeurIPS 2020 Batch Normalization Provably Avoids Ranks Collapse for Randomly Initialised Deep Networks Hadi Daneshmand, Jonas Kohler, Francis R. Bach, Thomas Hofmann, Aurelien Lucchi

ECCV 2020 Controlling Style and Semantics in Weakly-Supervised Image Generation Dario Pavllo, Aurelien Lucchi, Thomas Hofmann

NeurIPS 2020 Convolutional Generation of Textured 3D Meshes Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi

NeurIPS 2019 A Domain Agnostic Measure for Monitoring and Evaluating GANs Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause

NeurIPS 2019 Continuous-Time Models for Stochastic Optimization Algorithms Antonio Orvieto, Aurelien Lucchi

AISTATS 2019 Exponential Convergence Rates for Batch Normalization: The Power of Length-Direction Decoupling in Non-Convex Optimization Jonas Kohler, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr

AISTATS 2019 Local Saddle Point Optimization: A Curvature Exploitation Approach Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann

NeurIPS 2019 Shadowing Properties of Optimization Algorithms Antonio Orvieto, Aurelien Lucchi

UAI 2019 The Role of Memory in Stochastic Optimization Antonio Orvieto, Jonas Kohler, Aurelien Lucchi

ICML 2018 A Distributed Second-Order Algorithm You Can Trust Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi

ICLR 2018 An Online Learning Approach to Generative Adversarial Networks Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, Andreas Krause

ICML 2018 Escaping Saddles with Stochastic Gradients Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann

ICLR 2018 Semantic Interpolation in Implicit Models Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann

NeurIPS 2017 Stabilizing Training of Generative Adversarial Networks Through Regularization Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann

ICML 2017 Sub-Sampled Cubic Regularization for Non-Convex Optimization Jonas Moritz Kohler, Aurelien Lucchi

NeurIPS 2016 Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Alejandro Ribeiro

ICML 2016 Starting Small - Learning with Adaptive Sample Sizes Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann

NeurIPS 2015 Variance Reduced Stochastic Gradient Descent with Neighbors Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams

ICML 2013 An Optimal Policy for Target Localization with Application to Electron Microscopy Raphael Sznitman, Aurelien Lucchi, Peter Frazier, Bruno Jedynak, Pascal Fua

CVPR 2013 Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets Aurelien Lucchi, Yunpeng Li, Pascal Fua

ECCV 2012 Joint Image and Word Sense Discrimination for Image Retrieval Aurélien Lucchi, Jason Weston

ECCV 2012 Structured Image Segmentation Using Kernelized Features Aurélien Lucchi, Yunpeng Li, Kevin Smith, Pascal Fua

ICCV 2011 Are Spatial and Global Constraints Really Necessary for Segmentation? Aurélien Lucchi, Yunpeng Li, Xavier Boix Bosch, Kevin Smith, Pascal Fua