Aurélien Lucchi

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 103

Top Venues

Publications

Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, David Belius
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum. CoRR (2024)
Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Norbert Proske, Aurélien Lucchi
SDEs for Minimax Optimization. CoRR (2024)
Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurélien Lucchi
SDEs for Minimax Optimization. AISTATS (2024)
Emanuele Francazi, Aurélien Lucchi, Marco Baity-Jesi
Initial Guessing Bias: How Untrained Networks Favor Some Classes. CoRR (2023)
Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. CoRR (2023)
Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi
An SDE for Modeling SAM: Theory and Insights. ICML (2023)
Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann
Mastering Spatial Graph Prediction of Road Networks. ICCV (2023)
Enea Monzio Compagnoni, Antonio Orvieto, Luca Biggio, Hans Kersting, Frank Norbert Proske, Aurélien Lucchi
An SDE for Modeling SAM: Theory and Insights. CoRR (2023)
Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi
A Theoretical Analysis of the Learning Dynamics under Class Imbalance. ICML (2023)
Tin Sum Cheng, Aurélien Lucchi, Ivan Dokmanic, Anastasis Kratsios, David Belius
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. CoRR (2023)
Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. NeurIPS (2023)
Xuwei Yang, Anastasis Kratsios, Florian Krach, Matheus R. Grasselli, Aurélien Lucchi
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing. CoRR (2023)
Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, Ivan Dokmanic, David Belius
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. NeurIPS (2023)
Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf
Phenomenology of Double Descent in Finite-Width Neural Networks. CoRR (2022)
Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi
Anticorrelated Noise Injection for Improved Generalization. ICML (2022)
Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi
Generalization Through the Lens of Leave-One-Out Error. ICLR (2022)
Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. CoRR (2022)
Youssef Diouane, Aurélien Lucchi, Vihang Prakash Patil
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. AISTATS (2022)
Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf
Phenomenology of Double Descent in Finite-Width Neural Networks. ICLR (2022)
Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS (2022)
Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann
Mastering Spatial Graph Prediction of Road Networks. CoRR (2022)
Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi
Anticorrelated Noise Injection for Improved Generalization. CoRR (2022)
Youssef Diouane, Aurélien Lucchi, Vihang Patil
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. CoRR (2022)
Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. CoRR (2022)
Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS (2022)
Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi
Characterizing the Effect of Class Imbalance on the Learning Dynamics. CoRR (2022)
Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. NeurIPS (2022)
Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. NeurIPS (2022)
Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi
Generalization Through The Lens Of Leave-One-Out Error. CoRR (2022)
Christa Zoufal, Aurélien Lucchi, Stefan Woerner
Variational quantum Boltzmann machines. Quantum Mach. Intell. 3 (1) (2021)
Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi
Momentum Improves Optimization on Riemannian Manifolds. AISTATS (2021)
Sotiris Anagnostidis, Aurélien Lucchi, Youssef Diouane
Direct-Search for a Class of Stochastic Min-Max Problems. CoRR (2021)
Nathanaël Perraudin, Sandro Marcon, Aurélien Lucchi, Tomasz Kacprzak
Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks. Frontiers Artif. Intell. 4 (2021)
Aurélien Lucchi, Antonio Orvieto, Adamos Solomou
On the Second-order Convergence Properties of Random Search Methods. CoRR (2021)
Sotirios-Konstantinos Anagnostidis, Aurélien Lucchi, Youssef Diouane
Direct-Search for a Class of Stochastic Min-Max Problems. AISTATS (2021)
Antonio Orvieto, Jonas Köhler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR (2021)
Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo
Neural Symbolic Regression that scales. ICML (2021)
Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR (2021)
Dario Pavllo, Jonas Köhler, Thomas Hofmann, Aurélien Lucchi
Learning Generative Models of Textured 3D Meshes from Real-World Images. CoRR (2021)
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann
Generative Minimization Networks: Training GANs Without Competition. CoRR (2021)
Aurélien Lucchi, Antonio Orvieto, Adamos Solomou
On the Second-order Convergence Properties of Random Search Methods. NeurIPS (2021)
Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin
Scalable Graph Networks for Particle Simulations. AAAI (2021)
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, Stefan Woerner
The power of quantum neural networks. Nat. Comput. Sci. 1 (6) (2021)
Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi
Learning Generative Models of Textured 3D Meshes from Real-World Images. ICCV (2021)
Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo
Neural Symbolic Regression that Scales. CoRR (2021)
Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. AISTATS (2020)
Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi
Convolutional Generation of Textured 3D Meshes. CoRR (2020)
Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. NeurIPS (2020)
Celestine Mendler-Dünner, Aurélien Lucchi
Randomized Block-Diagonal Preconditioning for Parallel Learning. ICML (2020)
Yuwen Chen, Antonio Orvieto, Aurélien Lucchi
An Accelerated DFO Algorithm for Finite-sum Convex Functions. CoRR (2020)
Dario Pavllo, Aurélien Lucchi, Thomas Hofmann
Controlling Style and Semantics in Weakly-Supervised Image Generation. ECCV (6) (2020)
Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi
Convolutional Generation of Textured 3D Meshes. NeurIPS (2020)
Celestine Mendler-Dünner, Aurélien Lucchi
Randomized Block-Diagonal Preconditioning for Parallel Learning. CoRR (2020)
Yuwen Chen, Antonio Orvieto, Aurélien Lucchi
An Accelerated DFO Algorithm for Finite-sum Convex Functions. ICML (2020)
Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin
Scalable Graph Networks for Particle Simulations. CoRR (2020)
Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi
Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. CoRR (2020)
Nathanaël Perraudin, Sandro Marcon, Aurélien Lucchi, Tomasz Kacprzak
Emulation of cosmological mass maps with conditional generative adversarial networks. CoRR (2020)
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, Stefan Woerner
The power of quantum neural networks. CoRR (2020)
Antonio Orvieto, Aurélien Lucchi
Shadowing Properties of Optimization Algorithms. CoRR (2019)
Dario Pavllo, Aurélien Lucchi, Thomas Hofmann
Controlling Style and Semantics in Weakly-Supervised Image Generation. CoRR (2019)
Antonio Orvieto, Aurélien Lucchi
Continuous-time Models for Stochastic Optimization Algorithms. NeurIPS (2019)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS (2019)
Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann
Local Saddle Point Optimization: A Curvature Exploitation Approach. AISTATS (2019)
Antonio Orvieto, Aurélien Lucchi
Shadowing Properties of Optimization Algorithms. NeurIPS (2019)
Zuoyue Li, Jan Dirk Wegner, Aurélien Lucchi
Topological Map Extraction From Overhead Images. ICCV (2019)
Leonard Adolphs, Jonas Köhler, Aurélien Lucchi
Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training. CoRR (2019)
Antonio Orvieto, Jonas Köhler, Aurélien Lucchi
The Role of Memory in Stochastic Optimization. UAI (2019)
Antonio Orvieto, Jonas Köhler, Aurélien Lucchi
The Role of Memory in Stochastic Optimization. CoRR (2019)
Nathanaël Perraudin, Ankit Srivastava, Aurélien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Réfrégier
Cosmological N-body simulations: a challenge for scalable generative models. CoRR (2019)
Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. AISTATS (2019)
Aurélien Lucchi, Jonas Köhler
A Stochastic Tensor Method for Non-convex Optimization. CoRR (2019)
Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann
Towards a Theoretical Understanding of Batch Normalization. CoRR (2018)
Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann
Semantic Interpolation in Implicit Models. ICLR (Poster) (2018)
Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi
A Distributed Second-Order Algorithm You Can Trust. ICML (2018)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Thomas Hofmann, Andreas Krause
Evaluating GANs via Duality. CoRR (2018)
Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann
Adversarially Robust Training through Structured Gradient Regularization. CoRR (2018)
Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann
Escaping Saddles with Stochastic Gradients. CoRR (2018)
Antonio Orvieto, Aurélien Lucchi
Continuous-time Models for Stochastic Optimization Algorithms. CoRR (2018)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause
An Online Learning Approach to Generative Adversarial Networks. ICLR (Poster) (2018)
Zuoyue Li, Jan Dirk Wegner, Aurélien Lucchi
PolyMapper: Extracting City Maps using Polygons. CoRR (2018)
Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi
A Distributed Second-Order Algorithm You Can Trust. CoRR (2018)
Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann
Escaping Saddles with Stochastic Gradients. ICML (2018)
Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann
Local Saddle Point Optimization: A Curvature Exploitation Approach. CoRR (2018)
Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. WWW (2017)
Jonas Moritz Kohler, Aurélien Lucchi
Sub-sampled Cubic Regularization for Non-convex Optimization. CoRR (2017)
Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann
Flexible Prior Distributions for Deep Generative Models. CoRR (2017)
Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann
Stabilizing Training of Generative Adversarial Networks through Regularization. CoRR (2017)
Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann
Semantic Interpolation in Implicit Models. CoRR (2017)
Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann
Generator Reversal. CoRR (2017)
Joël Akeret, Chihway L. Chang, Aurélien Lucchi, Alexandre Réfrégier
Radio frequency interference mitigation using deep convolutional neural networks. Astron. Comput. 18 (2017)
Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler
Learning Aerial Image Segmentation from Online Maps. CoRR (2017)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause
An Online Learning Approach to Generative Adversarial Networks. CoRR (2017)
Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. CoRR (2017)
Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler
Learning Aerial Image Segmentation From Online Maps. IEEE Trans. Geosci. Remote. Sens. 55 (11) (2017)
Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann
Stabilizing Training of Generative Adversarial Networks through Regularization. NIPS (2017)
Jonas Moritz Kohler, Aurélien Lucchi
Sub-sampled Cubic Regularization for Non-convex Optimization. ICML (2017)
Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann
Starting Small - Learning with Adaptive Sample Sizes. ICML (2016)
Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann
Starting Small - Learning with Adaptive Sample Sizes. CoRR (2016)
Octavian-Eugen Ganea, Marina Ganea, Aurélien Lucchi, Carsten Eickhoff, Thomas Hofmann
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. WWW (2016)
Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. NIPS (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aurélien Lucchi