Add to ORKGModern machine learning often operates in the regime where the number of parameters is much higher than the number of data points, with zero training loss and yet good generalization, thereby contradicting the classical bias-variance trade-off. This \textit{benign overfitting} phenomenon has recently been characterized using so called \textit{double descent} curves where the risk undergoes another descent (in addition to the classical U-shaped learning curve when the number of parameters is small) as we increase the number of parameters beyond a certain threshold. In this paper, we examine the conditions under which \textit{Benign Overfitting} occurs in the random feature (RF) models, i.e. in a two-layer neural network with fixed first laye...
It is widely believed that the success of deep networks lies in their ability to learn a meaningful ...
The understanding of generalization in machine learning is in a state of flux. This is partly due to...
In classical statistics, the bias-variance trade-off describes how varying a model's complexity (e.g...
Modern neural networks often have great expressive power and can be trained to overfit the training ...
The recent success of neural network models has shone light on a rather surprising statistical pheno...
Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noi...
We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorl...
The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data while att...
In this work, we provide a characterization of the feature-learning process in two-layer ReLU networ...
The risk of overparameterized models, in particular deep neural networks, is often double-descent sh...
The remarkable practical success of deep learning has revealed some major surprises from a theoretic...
Injecting noise within gradient descent has several desirable features. In this paper, we explore no...
Recently, there has been an increase in literature about the Double Descent phenomenon for heavily o...
When a large feedforward neural network is trained on a small training set, it typically performs po...
The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodol...
It is widely believed that the success of deep networks lies in their ability to learn a meaningful ...
The understanding of generalization in machine learning is in a state of flux. This is partly due to...
In classical statistics, the bias-variance trade-off describes how varying a model's complexity (e.g...
Modern neural networks often have great expressive power and can be trained to overfit the training ...
The recent success of neural network models has shone light on a rather surprising statistical pheno...
Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noi...
We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorl...
The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data while att...
In this work, we provide a characterization of the feature-learning process in two-layer ReLU networ...
The risk of overparameterized models, in particular deep neural networks, is often double-descent sh...
The remarkable practical success of deep learning has revealed some major surprises from a theoretic...
Injecting noise within gradient descent has several desirable features. In this paper, we explore no...
Recently, there has been an increase in literature about the Double Descent phenomenon for heavily o...
When a large feedforward neural network is trained on a small training set, it typically performs po...
The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodol...
It is widely believed that the success of deep networks lies in their ability to learn a meaningful ...
The understanding of generalization in machine learning is in a state of flux. This is partly due to...
In classical statistics, the bias-variance trade-off describes how varying a model's complexity (e.g...