An online EM algorithm in hidden (semi-)Markov models for audio segmentation and clustering. - Alberto Bietti; Francis R. Bach; Arshia Cont (ICASSP, 2015)

Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure. - Alberto Bietti; Julien Mairal (NIPS, 2017)

Invariance and Stability of Deep Convolutional Representations. - Alberto Bietti; Julien Mairal (NIPS, 2017)

Group Invariance and Stability to Deformations of Deep Convolutional Representations. - Alberto Bietti; Julien Mairal (CoRR, 2017)

Practical Evaluation and Optimization of Contextual Bandit Algorithms. - Alberto Bietti; Alekh Agarwal; John Langford 001 (CoRR, 2018)

On Regularization and Robustness of Deep Neural Networks. - Alberto Bietti; Grégoire Mialon; Julien Mairal (CoRR, 2018)

A Kernel Perspective for Regularizing Deep Neural Networks. - Alberto Bietti; Grégoire Mialon; Dexiong Chen; Julien Mairal (ICML, 2019)

On the Inductive Bias of Neural Tangent Kernels. - Alberto Bietti; Julien Mairal (NeurIPS, 2019)

Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations. - Alberto Bietti; Julien Mairal (J. Mach. Learn. Res., 2019)

Foundations of deep convolutional models through kernel methods. (Méthodes à noyaux pour les réseaux convolutionnels profonds). - Alberto Bietti (2019)

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. - Nicolas Keriven; Alberto Bietti; Samuel Vaiter (NeurIPS, 2020)

Optimization Approaches for Counterfactual Risk Minimization with Continuous Actions. - Houssam Zenati; Alberto Bietti; Matthieu Martin; Eustache Diemert; Julien Mairal (CoRR, 2020)

Deep Equals Shallow for ReLU Networks in Kernel Regimes. - Alberto Bietti; Francis R. Bach (ICLR, 2021)

On Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Eric Vanden-Eijnden; Joan Bruna (ICML, 2021)

On the Sample Complexity of Learning under Geometric Stability. - Alberto Bietti; Luca Venturi; Joan Bruna (NeurIPS, 2021)

On the Universality of Graph Neural Networks on Large Random Graphs. - Nicolas Keriven; Alberto Bietti; Samuel Vaiter (NeurIPS, 2021)

On Approximation in Deep Convolutional Networks: a Kernel Perspective. - Alberto Bietti (CoRR, 2021)

On the Sample Complexity of Learning with Geometric Stability. - Alberto Bietti; Luca Venturi; Joan Bruna (CoRR, 2021)

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Marylou Gabrié; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2021)

A Contextual Bandit Bake-off. - Alberto Bietti; Alekh Agarwal; John Langford 001 (J. Mach. Learn. Res., 2021)

Efficient Kernelized UCB for Contextual Bandits. - Houssam Zenati; Alberto Bietti; Eustache Diemert; Julien Mairal; Matthieu Martin; Pierre Gaillard (AISTATS, 2022)

Approximation and Learning with Deep Convolutional Models: a Kernel Perspective. - Alberto Bietti (ICLR, 2022)

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. - Alberto Bietti; Chen-Yu Wei; Miroslav Dudík; John Langford 001; Zhiwei Steven Wu (ICML, 2022)

Learning single-index models with shallow neural networks. - Alberto Bietti; Joan Bruna; Clayton Sanford; Min Jae Song (NeurIPS, 2022)

When does return-conditioned supervised learning work for offline reinforcement learning? - David Brandfonbrener; Alberto Bietti; Jacob Buckman; Romain Laroche; Joan Bruna (NeurIPS, 2022)

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. - Alberto Bietti; Chen-Yu Wei; Miroslav Dudík; John Langford 001; Zhiwei Steven Wu (CoRR, 2022)

Efficient Kernel UCB for Contextual Bandits. - Houssam Zenati; Alberto Bietti; Eustache Diemert; Julien Mairal; Matthieu Martin; Pierre Gaillard (CoRR, 2022)

On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes. - Elvis Dohmatob; Alberto Bietti (CoRR, 2022)

On Minimal Variations for Unsupervised Representation Learning. - Vivien Cabannes; Alberto Bietti; Randall Balestriero (ICASSP, 2023)

The SSL Interplay: Augmentations, Inductive Bias, and Generalization. - Vivien Cabannes; Bobak Toussi Kiani; Randall Balestriero; Yann LeCun; Alberto Bietti (ICML, 2023)

Birth of a Transformer: A Memory Viewpoint. - Alberto Bietti; Vivien Cabannes; Diane Bouchacourt; Hervé Jégou; Léon Bottou (NeurIPS, 2023)

xVal: A Continuous Number Encoding for Large Language Models. - Siavash Golkar; Mariel Pettee; Michael Eickenberg; Alberto Bietti; Miles D. Cranmer; Géraud Krawezik; François Lanusse; Michael McCabe; Ruben Ohana; Liam Holden Parker; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Multiple Physics Pretraining for Physical Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (CoRR, 2023)

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. - François Lanusse; Liam Holden Parker; Siavash Golkar; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Géraud Krawezik; Michael McCabe; Ruben Ohana; Mariel Pettee; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

On Learning Gaussian Multi-index Models with Gradient Flow. - Alberto Bietti; Joan Bruna; Loucas Pillaud-Vivien (CoRR, 2023)

Scaling Laws for Associative Memories. - Vivien Cabannes; Elvis Dohmatob; Alberto Bietti (ICLR, 2024)

Learning Associative Memories with Gradient Descent. - Vivien Cabannes; Berfin Simsek; Alberto Bietti (ICML, 2024)

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. - Frederik Kunstner; Alan Milligan; Robin Yadav; Mark Schmidt 001; Alberto Bietti (NeurIPS, 2024)

Multiple Physics Pretraining for Spatiotemporal Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (NeurIPS, 2024)

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. - Siavash Golkar; Alberto Bietti; Mariel Pettee; Michael Eickenberg; Miles D. Cranmer; Keiya Hirashima; Géraud Krawezik; Nicholas Lourie; Michael McCabe; Rudy Morel; Ruben Ohana; Liam Holden Parker; et al. (CoRR, 2024)

How Truncating Weights Improves Reasoning in Language Models. - Lei Chen 062; Joan Bruna; Alberto Bietti (CoRR, 2024)

Level Set Teleportation: An Optimization Perspective. - Aaron Mishkin; Alberto Bietti; Robert M. Gower (AISTATS, 2025)

Learning Compositional Functions with Transformers from Easy-to-Hard Data. - Zixuan Wang; Eshaan Nichani; Alberto Bietti; Alex Damian; Daniel Hsu 001; Jason D. Lee; Denny Wu (COLT, 2025)

Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers. - Lei Chen 062; Joan Bruna; Alberto Bietti (ICLR, 2025)

Understanding Factual Recall in Transformers via Associative Memories. - Eshaan Nichani; Jason D. Lee; Alberto Bietti (ICLR, 2025)

Counterfactual Learning of Stochastic Policies with Continuous Actions. - Houssam Zenati; Alberto Bietti; Matthieu Martin; Eustache Diemert; Pierre Gaillard; Julien Mairal (Trans. Mach. Learn. Res., 2025)

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval. - Matthew Smart; Alberto Bietti; Anirvan M. Sengupta (CoRR, 2025)

BAnG: Bidirectional Anchored Generation for Conditional RNA Design. - Roman Klypa; Alberto Bietti; Sergei Grudinin (CoRR, 2025)