5 citations · 5 across the 2 of their papers we have counts for
8 papers
Matrix Discrepancy from Quantum Communication
Samuel B. Hopkins, Prasad Raghavendra, Abhishek Shetty
We develop a novel connection between discrepancy minimization and (quantum) communication complexity. As an application, we resolve a substantial special case of the Matrix Spence…
Smoothed Analysis with Adaptive Adversaries
Nika Haghtalab, Tim Roughgarden, Abhishek Shetty
We prove novel algorithmic guarantees for several online problems in the smoothed analysis model. In this model, at each time an adversary chooses an input distribution with densit…
Fractional Pseudorandom Generators from Any Fourier Level
Eshan Chattopadhyay, Jason Gaitonde, Chin Ho Lee +2
We prove new results on the polarizing random walk framework introduced in recent works of Chattopadhyay {et al.} [CHHL19,CHLT19] that exploit Fourier tail bounds for classes…
Smoothed Analysis of Online and Differentially Private Learning
Nika Haghtalab, Tim Roughgarden, Abhishek Shetty
Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms. The primary qu…
Effect of Activation Functions on the Training of Overparametrized Neural Nets
Abhishek Panigrahi, Abhishek Shetty, Navin Goyal
It is well-known that overparametrized neural networks trained using gradient-based methods quickly achieve small training error with appropriate hyperparameter settings. Recent pa…
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature
Navin Goyal, Abhishek Shetty
The Euclidean space notion of convex sets (and functions) generalizes to Riemannian manifolds in a natural sense and is called geodesic convexity. Extensively studied computational…