Search Results for author: Vineel Nagisetty

Found 7 papers, 1 papers with code

OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis

no code implementations2 Feb 2024 Lilian W. Bialokozowicz, Hoang M. Le, Tristan Sylvain, Peter A. I. Forsyth, Vineel Nagisetty, Greg Mori

This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis.

Survival Analysis

CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks

no code implementations4 Apr 2023 Vineel Nagisetty, Laura Graves, Guanting Pan, Piyush Jha, Vijay Ganesh

This functionality sets CGDTest apart from other similar DNN testing tools since it allows users to specify logical constraints to test DNNs not only for $\ell_p$ ball-based adversarial robustness but, more importantly, includes richer properties such as disguised and flow adversarial constraints, as well as adversarial robustness in the NLP domain.

Adversarial Robustness DNN Testing

A Solver + Gradient Descent Training Algorithm for Deep Neural Networks

no code implementations7 Jul 2022 Dhananjay Ashok, Vineel Nagisetty, Christopher Srinivasa, Vijay Ganesh

We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP) solver, outperforming GD and variants in terms of accuracy, as well as resource and data efficiency for both regression and classification tasks.

regression

Amnesiac Machine Learning

no code implementations21 Oct 2020 Laura Graves, Vineel Nagisetty, Vijay Ganesh

In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy.

BIG-bench Machine Learning

xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems

1 code implementation24 Feb 2020 Vineel Nagisetty, Laura Graves, Joseph Scott, Vijay Ganesh

A potential weakness in GANs is that it requires a lot of data for successful training and data collection can be an expensive process.

Explainable Artificial Intelligence (XAI)

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