10 citations · 28 across the 6 of their papers we have counts for
12 papers
Agnostic Learnability of Halfspaces via Logistic Loss
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi +2
We investigate approximation guarantees provided by logistic regression for the fundamental problem of agnostic learning of homogeneous halfspaces. Previously, for a certain broad…
Fast Margin Maximization via Dual Acceleration
Ziwei Ji, Nathan Srebro, Matus Telgarsky
We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes…
Early-stopped neural networks are consistent
Ziwei Ji, Justin D. Li, Matus Telgarsky
This work studies the behavior of shallow ReLU networks trained with the logistic loss via gradient descent on binary classification data where the underlying data distribution is…
Gradient descent follows the regularization path for general losses
Ziwei Ji, Miroslav Dudík, Robert E. Schapire +1
Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error,…
Directional convergence and alignment in deep learning
Ziwei Ji, Matus Telgarsky
In this paper, we show that although the minimizers of cross-entropy and related classification losses are off at infinity, network weights learned by gradient flow converge in dir…
Neural tangent kernels, transportation mappings, and universal approximation
Ziwei Ji, Matus Telgarsky, Ruicheng Xian
This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization…