46 citations · 151 across the 23 of their papers we have counts for
30 papers
On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme +2
Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust…
-Consistency Estimation Error of Surrogate Loss Minimizers
Pranjal Awasthi, Anqi Mao, Mehryar Mohri +1
We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as -consistency estimation error bounds, sin…
Agnostic Learnability of Halfspaces via Logistic Loss
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi +2
We investigate approximation guarantees provided by logistic regression for the fundamental problem of agnostic learning of homogeneous halfspaces. Previously, for a certain broad…
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…
Semi-supervised Active Regression
Fnu Devvrit, Nived Rajaraman, Pranjal Awasthi
Labelled data often comes at a high cost as it may require recruiting human labelers or running costly experiments. At the same time, in many practical scenarios, one already has a…
Neural Active Learning with Performance Guarantees
Pranjal Awasthi, Christoph Dann, Claudio Gentile +2
We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are stochastically generated from a class of functions on which w…