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ORCIDAlberto Bietti
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2020 – today
- 2024
[c20]Vivien Cabannes, Elvis Dohmatob, Alberto Bietti:
Scaling Laws for Associative Memories. ICLR 2024- [c19]Vivien Cabannes, Berfin Simsek, Alberto Bietti:
Learning Associative Memories with Gradient Descent. ICML 2024 - [i31]Vivien Cabannes, Berfin Simsek, Alberto Bietti:
Learning Associative Memories with Gradient Descent. CoRR abs/2402.18724 (2024) - [i30]Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti:
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. CoRR abs/2402.19449 (2024) - [i29]Aaron Mishkin, Alberto Bietti, Robert M. Gower:
Level Set Teleportation: An Optimization Perspective. CoRR abs/2403.03362 (2024) - [i28]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, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Shirley Ho:
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. CoRR abs/2406.02585 (2024) - [i27]Lei Chen, Joan Bruna, Alberto Bietti:
How Truncating Weights Improves Reasoning in Language Models. CoRR abs/2406.03068 (2024) - 2023
- [c18]Vivien Cabannes, Alberto Bietti, Randall Balestriero:
On Minimal Variations for Unsupervised Representation Learning. ICASSP 2023: 1-5 - [c17]Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. ICML 2023: 3252-3298 - [c16]Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Hervé Jégou, Léon Bottou:
Birth of a Transformer: A Memory Viewpoint. NeurIPS 2023 - [i26]Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. CoRR abs/2302.02774 (2023) - [i25]Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Hervé Jégou, Léon Bottou:
Birth of a Transformer: A Memory Viewpoint. CoRR abs/2306.00802 (2023) - [i24]Vivien Cabannes, Elvis Dohmatob, Alberto Bietti:
Scaling Laws for Associative Memories. CoRR abs/2310.02984 (2023) - [i23]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, Kyunghyun Cho, Shirley Ho:
xVal: A Continuous Number Encoding for Large Language Models. CoRR abs/2310.02989 (2023) - [i22]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, Kyunghyun Cho, Shirley Ho:
Multiple Physics Pretraining for Physical Surrogate Models. CoRR abs/2310.02994 (2023) - [i21]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, Kyunghyun Cho, Shirley Ho:
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR abs/2310.03024 (2023) - [i20]Alberto Bietti, Joan Bruna, Loucas Pillaud-Vivien:
On Learning Gaussian Multi-index Models with Gradient Flow. CoRR abs/2310.19793 (2023) - 2022
- [c15]Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard:
Efficient Kernelized UCB for Contextual Bandits. AISTATS 2022: 5689-5720 - [c14]Alberto Bietti:
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective. ICLR 2022 - [c13]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML 2022: 1945-1962 - [c12]Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning single-index models with shallow neural networks. NeurIPS 2022 - [c11]David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? NeurIPS 2022 - [i19]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR abs/2202.05318 (2022) - [i18]Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard:
Efficient Kernel UCB for Contextual Bandits. CoRR abs/2202.05638 (2022) - [i17]Elvis Dohmatob, Alberto Bietti:
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes. CoRR abs/2203.11864 (2022) - [i16]David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? CoRR abs/2206.01079 (2022) - [i15]Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning Single-Index Models with Shallow Neural Networks. CoRR abs/2210.15651 (2022) - [i14]Vivien Cabannes, Alberto Bietti, Randall Balestriero:
On minimal variations for unsupervised representation learning. CoRR abs/2211.03782 (2022) - 2021
- [j2]Alberto Bietti, Alekh Agarwal, John Langford:
A Contextual Bandit Bake-off. J. Mach. Learn. Res. 22: 133:1-133:49 (2021) - [c10]Alberto Bietti, Francis R. Bach:
Deep Equals Shallow for ReLU Networks in Kernel Regimes. ICLR 2021 - [c9]Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. ICML 2021: 2771-2782 - [c8]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS 2021: 6960-6971 - [c7]Alberto Bietti, Luca Venturi, Joan Bruna:
On the Sample Complexity of Learning under Geometric Stability. NeurIPS 2021: 18673-18684 - [i13]Alberto Bietti:
On Approximation in Deep Convolutional Networks: a Kernel Perspective. CoRR abs/2102.10032 (2021) - [i12]Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR abs/2104.07531 (2021) - [i11]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. CoRR abs/2105.13099 (2021) - [i10]Alberto Bietti, Luca Venturi, Joan Bruna:
On the Sample Complexity of Learning with Geometric Stability. CoRR abs/2106.07148 (2021) - [i9]Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié, Joan Bruna, Eric Vanden-Eijnden:
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR abs/2107.05134 (2021) - 2020
- [c6]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS 2020 - [i8]Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Julien Mairal:
Optimization Approaches for Counterfactual Risk Minimization with Continuous Actions. CoRR abs/2004.11722 (2020) - [i7]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR abs/2006.01868 (2020) - [i6]Alberto Bietti, Francis R. Bach:
Deep Equals Shallow for ReLU Networks in Kernel Regimes. CoRR abs/2009.14397 (2020)
2010 – 2019
- 2019
- [b1]Alberto Bietti:
Foundations of deep convolutional models through kernel methods. (Méthodes à noyaux pour les réseaux convolutionnels profonds). Grenoble Alpes University, France, 2019 - [j1]Alberto Bietti, Julien Mairal:
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations. J. Mach. Learn. Res. 20: 25:1-25:49 (2019) - [c5]Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal:
A Kernel Perspective for Regularizing Deep Neural Networks. ICML 2019: 664-674 - [c4]Alberto Bietti, Julien Mairal:
On the Inductive Bias of Neural Tangent Kernels. NeurIPS 2019: 12873-12884 - [i5]Alberto Bietti, Julien Mairal:
On the Inductive Bias of Neural Tangent Kernels. CoRR abs/1905.12173 (2019) - 2018
- [i4]Alberto Bietti, Alekh Agarwal, John Langford:
Practical Evaluation and Optimization of Contextual Bandit Algorithms. CoRR abs/1802.04064 (2018) - [i3]Alberto Bietti, Grégoire Mialon, Julien Mairal:
On Regularization and Robustness of Deep Neural Networks. CoRR abs/1810.00363 (2018) - 2017
- [c3]Alberto Bietti, Julien Mairal:
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure. NIPS 2017: 1623-1633 - [c2]Alberto Bietti, Julien Mairal:
Invariance and Stability of Deep Convolutional Representations. NIPS 2017: 6210-6220 - [i2]Alberto Bietti, Julien Mairal:
Group Invariance and Stability to Deformations of Deep Convolutional Representations. CoRR abs/1706.03078 (2017) - 2016
- [i1]Alberto Bietti, Julien Mairal:
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure. CoRR abs/1610.00970 (2016) - 2015
- [c1]Alberto Bietti, Francis R. Bach, Arshia Cont:
An online EM algorithm in hidden (semi-)Markov models for audio segmentation and clustering. ICASSP 2015: 1881-1885
Coauthor Index
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last updated on 2024-10-07 21:14 CEST by the dblp team
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