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07/28/2023
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
We study the asymptotic overfitting behavior of interpolation with minim...
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07/04/2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
Memorization of training data is an active research area, yet our unders...
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06/22/2023
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression
We study the cost of overfitting in noisy kernel ridge regression (KRR),...
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05/25/2023
Most Neural Networks Are Almost Learnable
We present a PTAS for learning random constant-depth networks. We show t...
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05/05/2023
Reconstructing Training Data from Multiclass Neural Networks
Reconstructing samples from the training set of trained neural networks ...
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03/02/2023
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization
Linear classifiers and leaky ReLU networks trained by gradient flow on t...
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03/02/2023
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
In this work, we study the implications of the implicit bias of gradient...
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03/01/2023
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds
Despite a great deal of research, it is still not well-understood why tr...
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02/15/2023
Efficiently Learning Neural Networks: What Assumptions May Suffice?
Understanding when neural networks can be learned efficiently is a funda...
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08/26/2022
On the Implicit Bias in Deep-Learning Algorithms
Gradient-based deep-learning algorithms exhibit remarkable performance i...
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06/15/2022
Reconstructing Training Data from Trained Neural Networks
Understanding to what extent neural networks memorize training data is a...
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05/18/2022
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
We study the dynamics and implicit bias of gradient flow (GF) on univari...
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02/13/2022
The Sample Complexity of One-Hidden-Layer Neural Networks
We study norm-based uniform convergence bounds for neural networks, aimi...
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02/09/2022
Gradient Methods Provably Converge to Non-Robust Networks
Despite a great deal of research, it is still unclear why neural network...
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02/08/2022
Width is Less Important than Depth in ReLU Neural Networks
We solve an open question from Lu et al. (2017), by showing that any tar...
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01/30/2022
Implicit Regularization Towards Rank Minimization in ReLU Networks
We study the conjectured relationship between the implicit regularizatio...
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10/07/2021
On the Optimal Memorization Power of ReLU Neural Networks
We study the memorization power of feedforward ReLU neural networks. We ...
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10/06/2021
On Margin Maximization in Linear and ReLU Networks
The implicit bias of neural networks has been extensively studied in rec...
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06/02/2021
Learning a Single Neuron with Bias Using Gradient Descent
We theoretically study the fundamental problem of learning a single neur...
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01/30/2021
Size and Depth Separation in Approximating Natural Functions with Neural Networks
When studying the expressive power of neural networks, a main challenge ...
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01/20/2021
From Local Pseudorandom Generators to Hardness of Learning
We prove hardness-of-learning results under a well-studied assumption on...
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12/09/2020
Implicit Regularization in ReLU Networks with the Square Loss
Understanding the implicit regularization (or implicit bias) of gradient...
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06/05/2020
Hardness of Learning Neural Networks with Natural Weights
Neural networks are nowadays highly successful despite strong hardness r...
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05/31/2020
Neural Networks with Small Weights and Depth-Separation Barriers
In studying the expressiveness of neural networks, an important question...
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06/15/2018