5 citations · 11 across the 4 of their papers we have counts for
11 papers
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds
Burak Bartan, Mert Pilanci
We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage random…
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Burak Bartan, Mert Pilanci
Neural networks (NNs) have been extremely successful across many tasks in machine learning. Quantization of NN weights has become an important topic due to its impact on their ener…
Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Time
Burak Bartan, Mert Pilanci
The training of two-layer neural networks with nonlinear activation functions is an important non-convex optimization problem with numerous applications and promising performance i…
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michał Dereziński, Burak Bartan, Mert Pilanci +1
In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data. However, the local…
Distributed Averaging Methods for Randomized Second Order Optimization
Burak Bartan, Mert Pilanci
We consider distributed optimization problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We develop unbiased parameter…
Distributed Sketching Methods for Privacy Preserving Regression
Burak Bartan, Mert Pilanci
In this work, we study distributed sketching methods for large scale regression problems. We leverage multiple randomized sketches for reducing the problem dimensions as well as pr…