3 citations · 3 across the 2 of their papers we have counts for
3 papers
stat.ML2019
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent
Tomer Lancewicki, Selcuk Kopru
Stochastic Gradient Descent (SGD) methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter…
stat.CO2017
Sequential Inverse Approximation of a Regularized Sample Covariance Matrix
Tomer Lancewicki
One of the goals in scaling sequential machine learning methods pertains to dealing with high-dimensional data spaces. A key related challenge is that many methods heavily depend o…
stat.CO2017★ 3 cited
Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation
Tomer Lancewicki
The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner p…