Agnostic Learning of General ReLU Activation Using Gradient Descent

no code implementations • 4 Aug 2022 • Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan

We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions.

Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations

no code implementations • NeurIPS 2021 • 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 assumptions.

Amazon SageMaker Autopilot: a white box AutoML solution at scale

no code implementations • 15 Dec 2020 • Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel

AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.

AutoML Meta-Learning