17 citations · 49 across the 16 of their papers we have counts for
7 papers · 1 filter
Agnostic Learning of General ReLU Activation Using Gradient Descent
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function with moderate bias under Gaussian distributions. Unlike prior…
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
We present polynomial time and sample efficient algorithms for learning an unknown depth-2 feedforward neural network with general ReLU activations, under mild non-degeneracy assum…
Adversarial robustness via robust low rank representations
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat +1
Adversarial robustness measures the susceptibility of a classifier to imperceptible perturbations made to the inputs at test time. In this work we highlight the benefits of natural…
Estimating Principal Components under Adversarial Perturbations
Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan
Robustness is a key requirement for widespread deployment of machine learning algorithms, and has received much attention in both statistics and computer science. We study a natura…
On Robustness to Adversarial Examples and Polynomial Optimization
Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
We study the design of computationally efficient algorithms with provable guarantees, that are robust to adversarial (test time) perturbations. While there has been an proliferatio…
Towards Learning Sparsely Used Dictionaries with Arbitrary Supports
Pranjal Awasthi, Aravindan Vijayaraghavan
Dictionary learning is a popular approach for inferring a hidden basis or dictionary in which data has a sparse representation. Data generated from the dictionary A (an n by m matr…