53 citations · 315 across the 37 of their papers we have counts for
6 papers · 1 filter
Tight Hardness Results for Training Depth-2 ReLU Networks
Surbhi Goel, Adam Klivans, Pasin Manurangsi +1
We prove several hardness results for training depth-2 neural networks with the ReLU activation function; these networks are simply weighted sums (that may include negative coeffic…
From Boltzmann Machines to Neural Networks and Back Again
Surbhi Goel, Adam Klivans, Frederic Koehler
Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult. In this wor…
Statistical-Query Lower Bounds via Functional Gradients
Surbhi Goel, Aravind Gollakota, Adam Klivans
We give the first statistical-query lower bounds for agnostically learning any non-polynomial activation with respect to Gaussian marginals (e.g., ReLU, sigmoid, sign). For the spe…
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel, Aravind Gollakota, Zhihan Jin +2
We prove the first superpolynomial lower bounds for learning one-layer neural networks with respect to the Gaussian distribution using gradient descent. We show that any classifier…
Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas +1
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions…
Approximation Schemes for ReLU Regression
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar +2
We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distri…