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06/01/2023
Birth of a Transformer: A Memory Viewpoint
Large language models based on transformers have achieved great empirica...
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02/06/2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Self-supervised learning (SSL) has emerged as a powerful framework to le...
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11/07/2022
On minimal variations for unsupervised representation learning
Unsupervised representation learning aims at describing raw data efficie...
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10/27/2022
Learning Single-Index Models with Shallow Neural Networks
Single-index models are a class of functions given by an unknown univari...
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06/02/2022
When does return-conditioned supervised learning work for offline reinforcement learning?
Several recent works have proposed a class of algorithms for the offline...
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03/22/2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes
Neural networks are known to be highly sensitive to adversarial examples...
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02/11/2022
Efficient Kernel UCB for Contextual Bandits
In this paper, we tackle the computational efficiency of kernelized UCB ...
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02/10/2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Large-scale machine learning systems often involve data distributed acro...
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07/11/2021
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
Energy-based models (EBMs) are generative models that are usually traine...
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06/14/2021
On the Sample Complexity of Learning with Geometric Stability
Many supervised learning problems involve high-dimensional data such as ...
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05/27/2021
On the Universality of Graph Neural Networks on Large Random Graphs
We study the approximation power of Graph Neural Networks (GNNs) on late...
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04/15/2021
On Energy-Based Models with Overparametrized Shallow Neural Networks
Energy-based models (EBMs) are a simple yet powerful framework for gener...
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02/19/2021
On Approximation in Deep Convolutional Networks: a Kernel Perspective
The success of deep convolutional networks on on tasks involving high-di...
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09/30/2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes
Deep networks are often considered to be more expressive than shallow on...
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06/02/2020
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
We study properties of Graph Convolutional Networks (GCNs) by analyzing ...
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04/22/2020
Optimization Approaches for Counterfactual Risk Minimization with Continuous Actions
Counterfactual reasoning from logged data has become increasingly import...
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05/29/2019
On the Inductive Bias of Neural Tangent Kernels
State-of-the-art neural networks are heavily over-parameterized, making ...
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09/30/2018
On Regularization and Robustness of Deep Neural Networks
Despite their success, deep neural networks suffer from several drawback...
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02/12/2018
Practical Evaluation and Optimization of Contextual Bandit Algorithms
We study and empirically optimize contextual bandit learning, exploratio...
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06/09/2017
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
In this paper, we study deep signal representations that are invariant t...
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10/04/2016