Physical space and division of labor around a tabletop tangible simulation. - Patrick Jermann; Guillaume Zufferey; Bertrand Schneider; Aurélien Lucchi; Simon Lépine; Pierre Dillenbourg (CSCL (1), 2009)

TinkerSheets: using paper forms to control and visualize tangible simulations. - Guillaume Zufferey; Patrick Jermann; Aurélien Lucchi; Pierre Dillenbourg (TEI, 2009)

A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images. - Aurélien Lucchi; Kevin Smith 001; Radhakrishna Achanta; Vincent Lepetit; Pascal Fua (MICCAI (2), 2010)

An empirical evaluation of touch and tangible interfaces for tabletop displays. - Aurélien Lucchi; Patrick Jermann; Guillaume Zufferey; Pierre Dillenbourg (TEI, 2010)

Are spatial and global constraints really necessary for segmentation? - Aurélien Lucchi; Yunpeng Li 002; Xavier Boix Bosch; Kevin Smith 001; Pascal Fua (ICCV, 2011)

Structured Image Segmentation Using Kernelized Features. - Aurélien Lucchi; Yunpeng Li 002; Kevin Smith 001; Pascal Fua (ECCV (2), 2012)

Joint Image and Word Sense Discrimination for Image Retrieval. - Aurélien Lucchi; Jason Weston (ECCV (1), 2012)

Efficient Scanning for EM Based Target Localization. - Raphael Sznitman; Aurélien Lucchi; Natasa Pjescic-Emedji; Graham Knott; Pascal Fua (MICCAI (3), 2012)

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. - Radhakrishna Achanta; Appu Shaji; Kevin Smith 001; Aurélien Lucchi; Pascal Fua; Sabine Süsstrunk (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features. - Aurélien Lucchi; Kevin Smith 001; Radhakrishna Achanta; Graham Knott; Pascal Fua (IEEE Trans. Medical Imaging, 2012)

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets. - Aurélien Lucchi; Yunpeng Li 002; Pascal Fua (CVPR, 2013)

An Optimal Policy for Target Localization with Application to Electron Microscopy. - Raphael Sznitman; Aurélien Lucchi; Peter I. Frazier; Bruno Jedynak; Pascal Fua (ICML (1), 2013)

Flash Scanning Electron Microscopy. - Raphael Sznitman; Aurélien Lucchi; Marco Cantoni; Graham Knott; Pascal Fua (MICCAI (3), 2013)

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images. - Aurélien Lucchi (2013)

Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation. - Aurélien Lucchi; Carlos J. Becker; Pablo Márquez-Neila; Pascal Fua (MICCAI (1), 2014)

Variance Reduced Stochastic Gradient Descent with Neighbors. - Thomas Hofmann 001; Aurélien Lucchi; Simon Lacoste-Julien; Brian McWilliams (NIPS, 2015)

Neighborhood Watch: Stochastic Gradient Descent with Neighbors. - Thomas Hofmann 001; Aurélien Lucchi; Brian McWilliams (CoRR, 2015)

A Variance Reduced Stochastic Newton Method. - Aurélien Lucchi; Brian McWilliams; Thomas Hofmann 001 (CoRR, 2015)

Learning Structured Models for Segmentation of 2-D and 3-D Imagery. - Aurélien Lucchi; Pablo Márquez-Neila; Carlos J. Becker; Yunpeng Li 002; Kevin Smith 001; Graham Knott; Pascal Fua (IEEE Trans. Medical Imaging, 2015)

Starting Small - Learning with Adaptive Sample Sizes. - Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001 (ICML, 2016)

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. - Aryan Mokhtari; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001; Alejandro Ribeiro (NIPS, 2016)

SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision. - Jan Deriu; Maurice Gonzenbach; Fatih Uzdilli; Aurélien Lucchi; Valeria De Luca; Martin Jaggi (SemEval@NAACL-HLT, 2016)

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. - Octavian-Eugen Ganea; Marina Ganea; Aurélien Lucchi; Carsten Eickhoff; Thomas Hofmann 001 (WWW, 2016)

Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning. - Wenhu Chen; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2016)

DynaNewton - Accelerating Newton's Method for Machine Learning. - Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2016)

Sub-sampled Cubic Regularization for Non-convex Optimization. - Jonas Moritz Kohler; Aurélien Lucchi (ICML, 2017)

Stabilizing Training of Generative Adversarial Networks through Regularization. - Kevin Roth; Aurélien Lucchi; Sebastian Nowozin; Thomas Hofmann 001 (NIPS, 2017)

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. - Jan Deriu; Aurélien Lucchi; Valeria De Luca; Aliaksei Severyn; Simon Müller; Mark Cieliebak; Thomas Hofmann 001; Martin Jaggi (WWW, 2017)

Radio frequency interference mitigation using deep convolutional neural networks. - Joël Akeret; Chihway L. Chang; Aurélien Lucchi; Alexandre Réfrégier (Astron. Comput., 2017)

Generator Reversal. - Yannic Kilcher; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2017)

Flexible Prior Distributions for Deep Generative Models. - Yannic Kilcher; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2017)

Learning Aerial Image Segmentation From Online Maps. - Pascal Kaiser; Jan Dirk Wegner; Aurélien Lucchi; Martin Jaggi; Thomas Hofmann 001; Konrad Schindler (IEEE Trans. Geosci. Remote. Sens., 2017)

An Online Learning Approach to Generative Adversarial Networks. - Paulina Grnarova; Kfir Y. Levy; Aurélien Lucchi; Thomas Hofmann 001; Andreas Krause 001 (ICLR (Poster), 2018)

Semantic Interpolation in Implicit Models. - Yannic Kilcher; Aurélien Lucchi; Thomas Hofmann 001 (ICLR (Poster), 2018)

Escaping Saddles with Stochastic Gradients. - Hadi Daneshmand; Jonas Moritz Kohler; Aurélien Lucchi; Thomas Hofmann 001 (ICML, 2018)

A Distributed Second-Order Algorithm You Can Trust. - Celestine Dünner; Aurélien Lucchi; Matilde Gargiani; An Bian; Thomas Hofmann 001; Martin Jaggi (ICML, 2018)

Adversarially Robust Training through Structured Gradient Regularization. - Kevin Roth; Aurélien Lucchi; Sebastian Nowozin; Thomas Hofmann 001 (CoRR, 2018)

Towards a Theoretical Understanding of Batch Normalization. - Jonas Moritz Kohler; Hadi Daneshmand; Aurélien Lucchi; Ming Zhou 007; Klaus Neymeyr; Thomas Hofmann 001 (CoRR, 2018)

Evaluating GANs via Duality. - Paulina Grnarova; Kfir Y. Levy; Aurélien Lucchi; Nathanaël Perraudin; Thomas Hofmann 001; Andreas Krause 001 (CoRR, 2018)

PolyMapper: Extracting City Maps using Polygons. - Zuoyue Li; Jan Dirk Wegner; Aurélien Lucchi (CoRR, 2018)

Local Saddle Point Optimization: A Curvature Exploitation Approach. - Leonard Adolphs; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001 (AISTATS, 2019)

Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. - Jonas Moritz Kohler; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001; Ming Zhou 007; Klaus Neymeyr (AISTATS, 2019)

Topological Map Extraction From Overhead Images. - Zuoyue Li; Jan Dirk Wegner; Aurélien Lucchi (ICCV, 2019)

A Domain Agnostic Measure for Monitoring and Evaluating GANs. - Paulina Grnarova; Kfir Y. Levy; Aurélien Lucchi; Nathanaël Perraudin; Ian J. Goodfellow; Thomas Hofmann 001; Andreas Krause 001 (NeurIPS, 2019)

Continuous-time Models for Stochastic Optimization Algorithms. - Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2019)

Shadowing Properties of Optimization Algorithms. - Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2019)

The Role of Memory in Stochastic Optimization. - Antonio Orvieto; Jonas Kohler; Aurélien Lucchi (UAI, 2019)

Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training. - Leonard Adolphs; Jonas Kohler; Aurélien Lucchi (CoRR, 2019)

Cosmological N-body simulations: a challenge for scalable generative models. - Nathanaël Perraudin; Ankit Srivastava; Aurélien Lucchi; Tomasz Kacprzak; Thomas Hofmann 001; Alexandre Réfrégier (CoRR, 2019)

A Stochastic Tensor Method for Non-convex Optimization. - Aurélien Lucchi; Jonas Kohler (CoRR, 2019)

A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. - Foivos Alimisis; Antonio Orvieto; Gary Bécigneul; Aurélien Lucchi (AISTATS, 2020)

Controlling Style and Semantics in Weakly-Supervised Image Generation. - Dario Pavllo; Aurélien Lucchi; Thomas Hofmann 001 (ECCV (6), 2020)

An Accelerated DFO Algorithm for Finite-sum Convex Functions. - Yuwen Chen; Antonio Orvieto; Aurélien Lucchi (ICML, 2020)

Randomized Block-Diagonal Preconditioning for Parallel Learning. - Celestine Mendler-Dünner; Aurélien Lucchi (ICML, 2020)

Batch normalization provably avoids ranks collapse for randomly initialised deep networks. - Hadi Daneshmand; Jonas Moritz Kohler; Francis R. Bach; Thomas Hofmann 001; Aurélien Lucchi (NeurIPS, 2020)

Convolutional Generation of Textured 3D Meshes. - Dario Pavllo; Graham Spinks; Thomas Hofmann 001; Marie-Francine Moens; Aurélien Lucchi (NeurIPS, 2020)

Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. - Hadi Daneshmand; Jonas Moritz Kohler; Francis R. Bach; Thomas Hofmann 001; Aurélien Lucchi (CoRR, 2020)

Scalable Graph Networks for Particle Simulations. - Karolis Martinkus; Aurélien Lucchi; Nathanaël Perraudin (AAAI, 2021)

Momentum Improves Optimization on Riemannian Manifolds. - Foivos Alimisis; Antonio Orvieto; Gary Bécigneul; Aurélien Lucchi (AISTATS, 2021)

Direct-Search for a Class of Stochastic Min-Max Problems. - Sotirios-Konstantinos Anagnostidis; Aurélien Lucchi; Youssef Diouane (AISTATS, 2021)

Learning Generative Models of Textured 3D Meshes from Real-World Images. - Dario Pavllo; Jonas Kohler; Thomas Hofmann 001; Aurélien Lucchi (ICCV, 2021)

Neural Symbolic Regression that scales. - Luca Biggio; Tommaso Bendinelli; Alexander Neitz; Aurélien Lucchi; Giambattista Parascandolo (ICML, 2021)

On the Second-order Convergence Properties of Random Search Methods. - Aurélien Lucchi; Antonio Orvieto; Adamos Solomou (NeurIPS, 2021)

Generative Minimization Networks: Training GANs Without Competition. - Paulina Grnarova; Yannic Kilcher; Kfir Y. Levy; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2021)

Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. - Antonio Orvieto; Jonas Kohler; Dario Pavllo; Thomas Hofmann 001; Aurélien Lucchi (CoRR, 2021)

Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks. - Nathanaël Perraudin; Sandro Marcon; Aurélien Lucchi; Tomasz Kacprzak (Frontiers Artif. Intell., 2021)

The power of quantum neural networks. - Amira Abbas; David Sutter; Christa Zoufal; Aurélien Lucchi; Alessio Figalli; Stefan Woerner (Nat. Comput. Sci., 2021)

Variational quantum Boltzmann machines. - Christa Zoufal; Aurélien Lucchi; Stefan Woerner (Quantum Mach. Intell., 2021)

A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. - Youssef Diouane; Aurélien Lucchi; Vihang Prakash Patil (AISTATS, 2022)

Vanishing Curvature in Randomly Initialized Deep ReLU Networks. - Antonio Orvieto; Jonas Kohler; Dario Pavllo; Thomas Hofmann 001; Aurélien Lucchi (AISTATS, 2022)

Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. - Junchi Yang; Antonio Orvieto; Aurélien Lucchi; Niao He (AISTATS, 2022)

Generalization Through the Lens of Leave-One-Out Error. - Gregor Bachmann; Thomas Hofmann 001; Aurélien Lucchi (ICLR, 2022)

Phenomenology of Double Descent in Finite-Width Neural Networks. - Sidak Pal Singh; Aurélien Lucchi; Thomas Hofmann 001; Bernhard Schölkopf (ICLR, 2022)

Anticorrelated Noise Injection for Improved Generalization. - Antonio Orvieto; Hans Kersting; Frank Proske; Francis R. Bach; Aurélien Lucchi (ICML, 2022)

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

Characterizing the Effect of Class Imbalance on the Learning Dynamics. - Emanuele Francazi; Marco Baity-Jesi; Aurélien Lucchi (CoRR, 2022)

Mastering Spatial Graph Prediction of Road Networks. - Sotiris Anagnostidis; Aurélien Lucchi; Thomas Hofmann 001 (ICCV, 2023)

An SDE for Modeling SAM: Theory and Insights. - Enea Monzio Compagnoni; Luca Biggio; Antonio Orvieto; Frank Norbert Proske; Hans Kersting; Aurélien Lucchi (ICML, 2023)

A Theoretical Analysis of the Learning Dynamics under Class Imbalance. - Emanuele Francazi; Marco Baity-Jesi; Aurélien Lucchi (ICML, 2023)

Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. - Sotiris Anagnostidis; Dario Pavllo; Luca Biggio; Lorenzo Noci; Aurélien Lucchi; Thomas Hofmann 001 (NeurIPS, 2023)

A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; Ivan Dokmanic; David Belius (NeurIPS, 2023)

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing. - Xuwei Yang; Anastasis Kratsios; Florian Krach; Matheus R. Grasselli; Aurélien Lucchi (CoRR, 2023)

SDEs for Minimax Optimization. - Enea Monzio Compagnoni; Antonio Orvieto; Hans Kersting; Frank Proske; Aurélien Lucchi (AISTATS, 2024)

Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; David Belius (ICML, 2024)

Initial Guessing Bias: How Untrained Networks Favor Some Classes. - Emanuele Francazi; Aurélien Lucchi; Marco Baity-Jesi (ICML, 2024)

A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; David Belius (NeurIPS, 2024)

Loss Landscape Characterization of Neural Networks without Over-Parametrization. - Rustem Islamov; Niccolò Ajroldi; Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2024)

Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks. - Jim Zhao; Sidak Pal Singh; Aurélien Lucchi (NeurIPS, 2024)

Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs. - Enea Monzio Compagnoni; Rustem Islamov; Frank Norbert Proske; Aurélien Lucchi (AISTATS, 2025)

Cubic regularized subspace Newton for non-convex optimization. - Jim Zhao; Nikita Doikov; Aurélien Lucchi (AISTATS, 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; Aurélien Lucchi (ICLR, 2025)

Optimization Guarantees for Square-Root Natural-Gradient Variational Inference. - Navish Kumar; Thomas Möllenhoff; Mohammad Emtiyaz Khan; Aurélien Lucchi (Trans. Mach. Learn. Res., 2025)

Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy. - Rustem Islamov; Samuel Horváth; Aurélien Lucchi; Peter Richtárik; Eduard Gorbunov (CoRR, 2025)

Where You Place the Norm Matters: From Prejudiced to Neutral Initializations. - Emanuele Francazi; Francesco Pinto; Aurélien Lucchi; Marco Baity-Jesi (CoRR, 2025)

When the Left Foot Leads to the Right Path: Bridging Initial Prejudice and Trainability. - Alberto Bassi; Carlo Albert; Aurélien Lucchi; Marco Baity-Jesi; Emanuele Francazi (CoRR, 2025)

Unpacking Softmax: How Temperature Drives Representation Collapse, Compression, and Generalization. - Wojciech Masarczyk; Mateusz Ostaszewski; Tin Sum Cheng; Tomasz Trzcinski; Aurélien Lucchi; Razvan Pascanu (CoRR, 2025)

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters. - Anastasis Kratsios; Tin Sum Cheng; Aurélien Lucchi; Haitz Sáez de Ocáriz Borde (CoRR, 2025)

Trainability of Quantum Models Beyond Known Classical Simulability. - Sabri Meyer; Francesco Scala; Francesco Tacchino; Aurélien Lucchi (CoRR, 2025)

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size. - Rustem Islamov; Niccolò Ajroldi; Antonio Orvieto; Aurélien Lucchi (CoRR, 2025)