← Authors

Alberto Bietti

28 papers · 2017–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (8) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (37) πŸŒ‰ Interdisciplinary Bridge πŸ‘‘ Triple Crown ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (74) πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (28) ❓ The Questioner

Conferences

NIPS (11) ICML (7) ICLR (5) AISTATS (2) JMLR (2) COLT (1)

Top co-authors

Julien Mairal (6) Joan Bruna (5) Vivien Cabannes (4) Samuel Vaiter (2) Nicolas Keriven (2) Eshaan Nichani (2) John Langford (2) Jason D. Lee (1) David Brandfonbrener (1) Shirley Ho (1)

Keywords

kernel methods (4) reproducing kernel hilbert space (3) representation learning (3) inductive bia (2) stochastic optimization (2) convolutional neural network (2) shallow neural network (2) random graph (2) contextual bandit (2) offline reinforcement learning (1) in-context learning (1) data augmentation (1) supervised learning (1) sample complexity (1) neural tangent kernel (1) self-supervised learning (1) stability analysis (1) graph learning (1) function space (1) computational efficiency (1)

Papers

Level Set Teleportation: An Optimization Perspective AISTATS 2025
Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers ICLR 2025
Understanding Factual Recall in Transformers via Associative Memories ICLR 2025
In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval ICML 2025
Learning Compositional Functions with Transformers from Easy-to-Hard Data COLT 2025
BAnG: Bidirectional Anchored Generation for Conditional RNA Design ICML 2025
Scaling Laws for Associative Memories ICLR 2024
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models NIPS 2024
Multiple Physics Pretraining for Spatiotemporal Surrogate Models NIPS 2024
Learning Associative Memories with Gradient Descent ICML 2024
Birth of a Transformer: A Memory Viewpoint NIPS 2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization ICML 2023
When does return-conditioned supervised learning work for offline reinforcement learning? NIPS 2022
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective ICLR 2022
Learning single-index models with shallow neural networks NIPS 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning ICML 2022
Efficient Kernelized UCB for Contextual Bandits AISTATS 2022
A Contextual Bandit Bake-off JMLR 2021
On the Universality of Graph Neural Networks on Large Random Graphs NIPS 2021
On the Sample Complexity of Learning under Geometric Stability NIPS 2021
Deep Equals Shallow for ReLU Networks in Kernel Regimes ICLR 2021
On Energy-Based Models with Overparametrized Shallow Neural Networks ICML 2021
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs NIPS 2020
A Kernel Perspective for Regularizing Deep Neural Networks ICML 2019
On the Inductive Bias of Neural Tangent Kernels NIPS 2019
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations JMLR 2019
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure NIPS 2017
Invariance and Stability of Deep Convolutional Representations NIPS 2017