4 papers
Efficient Techniques for Data Reconstruction, with Finite-Width Recovery Guarantees
Edward Tansley, Roy Makhlouf, Estelle Massart +1
Data reconstruction attacks on trained neural networks aim to recover the data on which the network has been trained and pose a significant threat to privacy, especially if the tra…
On the Neural Feature Ansatz for Deep Neural Networks
Edward Tansley, Estelle Massart, Coralia Cartis
Understanding feature learning is an important open question in establishing a mathematical foundation for deep neural networks. The Neural Feature Ansatz (NFA) states that after t…
Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions
Coralia Cartis, Zhen Shao, Edward Tansley
We propose and analyze random subspace variants of the second-order Adaptive Regularization using Cubics (ARC) algorithm. These methods iteratively restrict the search space to som…
Scalable Second-Order Optimization Algorithms for Minimizing Low-rank Functions
Edward Tansley, Coralia Cartis
We present a random-subspace variant of cubic regularization algorithm that chooses the size of the subspace adaptively, based on the rank of the projected second derivative matrix…