Alberto Bietti
28 papers · 2017–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (8) π Conference Polyglot (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (13)
π
Renaissance Researcher
(5)
πΊοΈ
Taxonomy Completionist
(37)
π
Interdisciplinary Bridge
π
Triple Crown
β‘
Prolific Year
(5)
ποΈ
Keyword Collector
(74)
π₯
Unstoppable
(7)
π
Century Club
(28)
β
The Questioner
Conferences
NIPS (11)
ICML (7)
ICLR (5)
AISTATS (2)
JMLR (2)
COLT (1)
Top co-authors
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